System and method of compiling and organizing power consumption data and converting such data into one or more user actionable formats

ABSTRACT

A method and system for use in creating a profile of, managing and understanding power consumption in a premise of a user, wherein said premise comprises two or more power consuming devices comprises measuring, via at least one sensor, aggregate energy consumption at the premise, receiving at a mobile computing device comprising a data processor, said aggregated signal from the sensor, collecting and recording the aggregate signal over a plurality of time resolutions and frequencies, therein to create a predicted aggregate signal for each time x and frequency y, detecting changes in the predicted aggregate signal at time x an frequency y (detected consumption pattern changes) and conveying to at least one of the user, a utility company, and other third party a notification of detected consumption pattern changes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/372,056, filed Jul. 14, 2014, which is the National Stage ofInternational Application No. PCT/CA2013/000062, filed Jan. 21, 2013,which in turn claims the benefit of U.S. Provisional Application No.61/589,203, filed on Jan. 20, 2012, all of which are herein incorporatedby reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to the field of granular power monitoring,data analytics and enhanced data use at both the consumer and industrylevels.

BACKGROUND OF THE INVENTION

Energy management is a term that generally relates to or is implementedby systems, processes and devices in order to reduce energy consumptionand understand energy consumption patterns. This can occur in privatehomes, in businesses, in factories/manufacturing facilities and inpublic-sector/government organizations, to name a few.

From the perspective of an energy consumer, the process of monitoring,controlling, and conserving energy in a building or organizationtypically involves the following steps, with noted challenges andlimitations:

1. Metering (in some fashion) energy consumption and collecting thedata.

2. Understanding the raw data and/or collecting data that is useful.

3. Finding opportunities to save energy, and estimating how much energyeach opportunity could save. For example, an individual could analyzeher meter data to find and quantify routine energy waste, and might alsoinvestigate the energy savings that could be made by replacing equipment(e.g. lighting) or by upgrading a building's insulation.

4. Taking action to target the opportunities to save energy (i.e.addressing the routine waste and replacing or upgrading inefficientequipment).

5. Tracking progress by analyzing meter data to see how well theenergy-saving efforts have worked.

At a consumer level, as the cost of energy/electricity continues toincrease, there is greater awareness of consumption issues and morethought put into sustainable energy planning. For example, people arebuying more high fuel efficiency cars including both smaller and hybridelectric cars.

However, in order for people to use less energy/electricity in theirhomes and businesses, they need to have some means to assess energyusage and to make appropriate adaptations and decisions. One approach inenergy-data collection is to manually read meters once a week or once amonth. This is not only onerous but of very limited use in terms of dataspread.

An alternative approach to energy-data collection is to installinterval-metering systems that automatically measure and record energyconsumption at short, regular intervals such as every hour, every15-minutes, or even every few seconds when needed. This detailedinterval energy consumption data makes it possible to see patterns ofenergy waste that it would be impossible to see otherwise: for exampleone can ascertain how much energy is being used at different times ofthe day or on different days of the week. Using the detailed intervaldata, it is possible to make broad brush estimates of how much energy isbeing wasted at different times. For example, if a person identifiesthat energy is being wasted by electronics left on over the weekends,one can:

a. Use interval data to calculate how much energy (in kWh) is being usedeach weekend.

b. Estimate the proportion of that energy that is being wasted (byelectronics that should be switched off).

c. Using the figures from a and b, calculate an estimate of the totalkWh that are wasted each weekend.

i. This type of data and information is in bulk “aggregate” form and isnot particular or granular.

Using power sensors on every device, it is possible to acquire anitemized bill that shows usage and energy cost for various appliances.With itemized data, consumers can take action to conserve, by eitherinstalling more energy efficient appliances (air conditioners, clotheswashers/dryers, hot tubs, ovens, lighting, etc. . . . ), or changingtheir usage patterns in areas where pricing of energy/electricity variesby time of day, or simply turning loads off when not in use. The problemis that people do not want to incur the significant expense required toinstall power sensors on each of their appliances and electric loads.This underscores the significant problems:

a) while there is some value to the bulk aggregate data, it is not thedefinitive picture in energy management, in fact, it barely scratchesthe surface of what should be possible and available to power consumers;andb) load disaggregation or cataloguing power usage at a granular level isdifficult to currently achieve. Even if power sensors are attached ontoevery single appliance in a home, there is still the issue of the valueof the produced raw data without further enhancements and value added.

From the perspective of the consumer, as opposed to utility companies,there are some overlapping but also different concerns in regards topower usage. With the advent of “smart grid” technologies, also called“smart home”, “smart meter”, or “home area network” (HAN) technologies,optimized demand reductions became possible at the end use or appliancelevel. Some smart grid technologies provided the ability to capturereal-time or near-real-time end-use data and enabled two-waycommunication. Smart grid technologies currently exist for at least somepercentage of a utility's customer base and applications are growingthroughout North America. From a consumer perspective, smart meteringoffers a number of potential benefits to householders. These include theprovision of a tool to help consumers better manage their energy use.Smart meters with a display can provide up to date information on gasand electricity consumption in the currency of that country and in doingso help people to better manage their energy use and reduce their energybills and carbon emissions.

Various “load disaggregation” (as defined below) algorithms have beensuggested in the literature. One technique of decomposing the powersignal measured at the incoming power meter into its constituentindividual loads is known as Single Point End-use Energy Disaggregation(SPEED™), and is available from Enetics, Inc. of New York. The SPEED™product includes logging premises load data and then transferring thedata via telephone, walk-ups, or alternative communications to a MasterStation that processes the recorder data into individual load intervaldata, acts as a server and database manager for pre and post processedenergy consumption data, temperature data, queries from analysisstations, and queries from other information systems. This knowntechnique runs on a Windows™ operating system.

While this improves the quality of decomposition techniques, there stillexists the need, at the consumer level in particular, for a simple andinexpensive power consumption monitoring system that does not require aMaster Station and/or additional people, resources to decompose anelectric power meter signal to its constituent individual loads.

From the perspective of utility companies and energy traders, there is aneed and demand to create demand projections and maintain a regulatedreserve margin above (but not too far above) such demand. The capacitythat is above or below that margin can be bought or sold in the energymarkets.

Furthermore, there is a growing tendency towards unbundling the powersystem as different sectors of the industry (generation, transmission,and distribution) are faced with increasing demand on planningmanagement and operations of the networks. The operation and planning ofa power utility company requires an adequate model for power loadforecasting. This load forecasting plays a key role in helping a utilityto make important decisions on power, load switching, voltage control,network reconfiguration, and infrastructure development.

Data acquired from a plurality of households, businesses and other powerconsuming entities as to behaviors and power consumption, in a granularform would be highly desired.

It is an object of the present invention to obviate or mitigate theabove disadvantages.

SUMMARY OF INVENTION

The present invention is directed generally to systems and methods formonitoring energy consumption and for related operations and, morespecifically, for monitoring of energy consumption in premises with aview to providing consumption awareness to users and premise managementsystems. The present invention has wide reaching uses and applicationsand may be used, for example, for non-intrusive load monitoring,electricity monitoring, energy monitoring, in-house energy management,building automation, and for other applications. As a result, thepresent invention may be commercialized by utilities or third-parties asa product that enables consumers to better manage their electricityconsumption. It can also be commercialized as a software solution fordata aggregators like Google™ (for example, via Google PowerMeter™).

The present invention may be implemented as an aggregate measurementsystem that non-intrusively detects which power consuming devices areturned on and off in a building and reports usage information to eitherthe user or to an automated energy management system or to a utility.The present invention may be implemented in many ways and may offer manybenefits, some examples of which are identified below.

The present invention provides, in one aspect, a method for use increating a profile of, managing and understanding power consumption in ahome of a user, wherein said home comprises two or more power consumingdevices which comprises:

a) measuring, via at least one sensor, aggregate energy consumption atthe home;b) receiving at a mobile computing device comprising a data processor,said aggregated signal from the sensor;c) collecting and recording the aggregate signal over a plurality oftime resolutions and frequencies, therein to create a predictedaggregate signal for each time x and frequency y;d) detecting changes in the predicted aggregate signal at time x anfrequency y (detected consumption pattern changes); ande) conveying to at least one of the user, a utility company, and otherthird party a notification of detected consumption pattern changes.

The present invention provides, in another aspect, an unsupervisedsystem for use in creating a profile of, managing and understandingpower consumption in a home of a user, wherein said home comprises twoor more power consuming devices which system comprises:

a) at least one sensor configured to measure aggregate energyconsumption at the home;b) a mobile computing device comprising a data processor;c) computer readable memory including computer readable instructionswhich, when executed by the processor, cause the processor to performthe following steps: i) receive said aggregated signal from the sensor;ii) collect and record the aggregate signal over a plurality of timeresolutions and frequencies, iii) create a predicted aggregate signalpattern for each time x and frequency y; vi) to detect changes in thepredicted aggregate signal pattern at time x an frequency y (detectedconsumption pattern changes); andd) a communication interface operably connected to the mobile computingdevice and configured for conveying to a user notification of detectedconsumption pattern changes.

The present invention provides, in another aspect, a system for use increating a profile of, managing and understanding power consumption in ahome, wherein said home comprises two or more power consuming deviceswhich system comprises:

a) at least one sensor configured to measure at least one energyconsumption variable associated with at least one energy consumptiondevice within the home (“the selected device”) and to generate at leastone aggregated output signal therefrom;b) a mobile computing device comprising a data processor;c) computer readable memory comprising memory comprising a catalogue ofa plurality of devices and one of a respective or estimated power drawof each such device, said memory including computer readableinstructions which, when executed by the processor, cause the processorto perform the following steps: i) receive said aggregated signal fromthe sensor; ii) create and update a power profile for the selecteddevice, iii) collect and analyze raw data in real time, iv) calculate adelta for each selected device (difference between an on state and anoff state); v) calculate an estimated delta for the selected device,using ON-OFF-ON sequences (or OFF-ON-OFF) thereby acquiring a startvalue and end value, and vi) comparing the start value and end value toassess reliability of the estimated delta for the selected device; andd) a communication interface operably connected to the mobile computingdevice and configured for receiving user commands and queries, forrequesting user input in respect to said devices and for transmittinginformation relating to the devices to the user.

The present invention provides, in another aspect, a system foracquiring and storing disaggregated power consumption data in a premiseswhich comprises:

a) at least one sensor configured to measure at least one desired energyconsumption variable associated with a plurality of energy consumptiondevices within the premises and to generate at least one aggregatedoutput signal therefrom;b) a data processor configured to receive said aggregated signal fromthe sensor; said processor comprising a means to create and update apower profile for each individual device, said data processor comprisinga memory which comprises a catalogue of each of said individual devicesand a respective power draw of each device.

The present invention provides, in another aspect, a computerimplemented method of acquiring, cataloguing and storing powerconsumption data in respect to a first energy consumption device (withan energy draw) within a premises comprising a plurality of energyconsumption devices which comprises:

a) providing a sensor configured to measure at least one desired energyconsumption variable associated with the plurality of energy consumptiondevices (including the first device) within the premises and to generateat least one aggregated output signal therefrom;b) configuring a data processor to receive said aggregated signal fromthe sensor;c) creating a power profile for the first device by instructing a user,via a user interface, to independently switch said device between on-offpositions (“switching set up”), at least one time, to isolate a powerdraw for said device from the aggregated signal, wherein data processorrecognizes that the first device was selected and isolates adifferential in the aggregate signal based on differing switch positionsduring the switching set up, said differential being the energy draw ofthe first device; andd) providing a memory which recallably stores the energy draw of thefirst device in a catalogue.

The present invention provides, in yet another aspect, a powerconsumption and notification system comprising:

a) at least one sensor configured to measure at least one desired energyconsumption variable associated with at least one energy consumptiondevice within a premises and to generate at least one aggregated outputsignal therefrom;b) a data processor configured to receive said aggregated signal fromthe sensor; said processor comprising a means to create and update apower profile for each at least said one device, said data processorcomprising a memory which comprises a catalogue of each of at least saidone device and a respective power draw of each such device, said dataprocessor including a means to collect and analyze raw data in realtime, from at least one of following sources: smart grid networks;current sensors; user inputs relating to user-defined budgets; userinputs relating to his behaviors and schedules; user inputs relating tothe function and activities of the devices; other user informationavailable through a networked device such as contacts, demographics,etc; GPS and other location signals such as WiFi network IDs, names andsignal strengths; macrogrid outputs from within a population in whichuser belongs; television and radio signals; memory based historicalconsumption data; said data processor including means to analyze,organize and reformat the raw data and to communicate to user based oninformation acquired from any of the sources; andc) a user interface.

The present invention provides, in yet another aspect, a non-transitoryprocessor readable medium storing code representing instructions tocause a processor to acquire, catalogue and store power consumption datain respect to a first energy consumption device (with an energy draw)within a premises comprising a plurality of energy consumption deviceswhich comprises:

a) providing a sensor configured to measure at least one desired energyconsumption variable associated with the plurality of energy consumptiondevices (including the first device) within the premises and to generateat least one aggregated output signal therefrom;b) configuring a data processor to receive said aggregated signal fromthe sensor;c) creating a power profile for the first device by instructing a user,via a user interface, to independently switch said device between on-offpositions (“switching set up”), at least one time, to isolate a powerdraw for said device from the aggregated signal, wherein data processorrecognizes that the first device was selected and isolates adifferential in the aggregate signal based on differing switch positionsduring the switching set up, said differential being the energy draw ofthe first device; andd) providing a memory which recallably stores the energy draw of thefirst device in a catalogue.

In one aspect, such a code comprises instructions to create a powerprofile for a second device by instructing a user, via a user interface,to independently switch said second device between on-off positions(“switching set up”), at least one time, to isolate a power draw forsaid second device from the aggregated signal, wherein data processorrecognizes that the second device was selected and isolates adifferential in the aggregate signal based on differing switch positionsduring the switching set up, said differential being the energy draw ofthe second device; and to provide a memory which recallably stores theenergy draw of the second device in a catalogue.

The method and system of the present invention affords many advantagesover the systems previously known. The use and criticality of thepresent innovation cannot be under-estimated: in order for people to useless energy/electricity in their homes, they need to have some means toassess energy usage and to make real time adaptations in a simple, costeffective way. The present invention, exemplified within the systems andmethods described and claimed herein, provides the solution.

From the perspective of mass data acquisition, at the granular level,utilities and power traders are demanding granular information in orderto assess energy consumption and to assess the impacts of suchconsumption on the electricity grid in terms of protection, control,cost efficiency and power quality issues.

As such the data analytics in accordance with the present inventionyield superior demand forecasts by “segmenting” user profiles andmodeling their consumption behavior separately using increased inputdata granularity. With access to real time segmented data, accurateshort term (and long term) demand projections are made more accuratelywhich affords significant cost saving to a utility and ultimately to aconsumer, whether that consumer be a family, a business or amanufacturing operation.

DESCRIPTION OF THE FIGURES

The following figures set forth embodiments in which like referencenumerals denote like parts. Embodiments are illustrated by way ofexample and not by way of limitation in the accompanying figures.

FIG. 1 is a schematic showing a smartphone dashboard (user interface) inaccordance with the present invention.

FIGS. 2A, 2B, 2C, 2D, 2E and 2F are schematics showing a sequence of sixuser interface smartphone dashboards which sequentially illustrate theswitching set up for an oven. FIG. 2A shows a prompt to turn a deviceswitch for the first time. FIG. 2B shows a prompt to input deviceinformation. FIG. 2C shows a prompt to input device information. FIG. 2Dshows a prompt to turn the device switch a second time. FIG. 2E shows aprompt to adjust device usage information. FIG. 2F shows a prompt tosave the information.

FIG. 3 is a graph showing real time usage of a device over time in a newdevice profile i.e. when device switched by user for first time.

FIG. 4 is a graph showing real time usage of a device over time in a newdevice profile i.e. when device switched on and then off by user.

FIG. 5 is a is a graph showing real time usage of a device over time ina new device profile i.e. when device switched on and then off by user,but showing unreliability.

FIG. 6 is graph showing real time usage of a device over time in a newdevice profile i.e. when device switched on and then off by user, butshowing challenge in observation due to noise.

FIGS. 7A, 7B and 7C are schematics showing a sequence of three userinterface smartphone dashboards which illustrate device cataloguescreenshots. FIG. 7A shows a list of all profiled devices. FIG. 7B showssocial information about each device. FIG. 7C shows a graphicalrepresentation for the catalogued devices.

FIG. 8 is a schematic showing a user interface Smartphone dashboardwhich illustrates opportunities for marketing and promotion inconveyance of information to user.

FIG. 9 is a graph showing the Mean and Standard Deviation over PeriodLength of One Day and Time-Resolution of One Hour.

FIG. 10 is a graph showing Mean and Standard Deviation over PeriodLength of One Week and Time-Resolution of One Day.

FIG. 11 is a graph showing Monthly Consumption Pattern, DemonstratingContinuous Changes over Time.

FIG. 12 is a series of graphs showing Daily-Hour Patterns.

FIG. 13 is a series of graphs showing Weekly-Day Patterns.

FIG. 14 is a graph showing the absence of detection Fuzzy Sets.

FIGS. 15A, 15B, 15C, 15D and 15E illustrate a series of graphical userinterface screens in typical interaction sequences between the system ofthe invention, on a mobile device, and a user of such a mobile device.FIG. 15A shows a welcome screen; FIG. 15B shows current householdconsumption; FIG. 15C shows a list of major energy consumers; FIG. 15Dshows bills; and FIG. 15E shows unusual energy consumption.

PREFERRED EMBODIMENTS OF THE INVENTION

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. As such this detailed descriptionillustrates the invention by way of example and not by way oflimitation. The description will clearly enable one skilled in the artto make and use the invention, and describes several embodiments,adaptations, variations and alternatives and uses of the invention,including what we presently believe is the best mode for carrying outthe invention. It is to be clearly understood that routine variationsand adaptations can be made to the invention as described, and suchvariations and adaptations squarely fall within the spirit and scope ofthe invention.

In other words, the invention is described in connection with suchembodiments, but the invention is not limited to any embodiment. Thescope of the invention is limited only by the claims and the inventionencompasses numerous alternatives, modifications and equivalents.Numerous specific details are set forth in the following description inorder to provide a thorough understanding of the invention. Thesedetails are provided for the purpose of example and the invention may bepracticed according to the claims without some or all of these specificdetails. For the purpose of clarity, technical material that is known inthe technical fields related to the invention has not been described indetail so that the invention is not unnecessarily obscured. Similarreference characters denote similar elements throughout various viewsdepicted in the figures.

Unless specifically stated otherwise, it is appreciated that throughoutthe description, discussions utilizing terms such as “processing” or“computing” or “calculating” or “determining” or “displaying” or thelike, refer to the action and processes of a data processing system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within a computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

The algorithms and displays with the applications described herein arenot inherently related to any particular computer or other apparatus.Various general-purpose systems may be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required machine-implemented methodoperations. The required structure for a variety of these systems willappear from the description below. In addition, embodiments of thepresent invention are not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages may be used to implement the teachings ofembodiments of the invention as described herein.

An embodiment of the invention may be implemented as a method or as amachine readable non-transitory storage medium that stores executableinstructions that, when executed by a data processing system, causes thesystem to perform a method. An apparatus, such as a data processingsystem, can also be an embodiment of the invention. Other features ofthe present invention will be apparent from the accompanying drawingsand from the detailed description which follows.

TERMS

The term “invention” and the like mean “the one or more inventionsdisclosed in this application”, unless expressly specified otherwise.

The terms “an aspect”, “an embodiment”, “embodiment”, “embodiments”,“the embodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, “certain embodiments”, “one embodiment”, “anotherembodiment” and the like mean “one or more (but not all) embodiments ofthe disclosed invention(s)”, unless expressly specified otherwise.

The term “variation” of an invention means an embodiment of theinvention, unless expressly specified otherwise.

The terms “mobile device” or “mobile processing device” both referherein interchangeably to any computer (for example desk top or laptopcomputers), microprocessing device, personal digital assistant,SmartPhone other cell phone, tablets and the like. Preferably, devicescomprise iPhones™, iPADS™, other devices operating via iOS™ or MAC OS™,or devices operating on Android™ OS.

A reference to “another embodiment” or “another aspect” in describing anembodiment does not imply that the referenced embodiment is mutuallyexclusive with another embodiment (e.g., an embodiment described beforethe referenced embodiment), unless expressly specified otherwise.

The terms “including”, “comprising” and variations thereof mean“including but not limited to”, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

The term “plurality” means “two or more”, unless expressly specifiedotherwise.

The term “herein” means “in the present application, including anythingwhich may be incorporated by reference”, unless expressly specifiedotherwise.

The term “habit”, as used herein refers to a recurrent, conscious oroften unconscious pattern of behavior that is acquired through frequentrepetition and includes customary manners or practices of the user.

The term “whereby” is used herein only to precede a clause or other setof words that express only the intended result, objective or consequenceof something that is previously and explicitly recited. Thus, when theterm “whereby” is used in a claim, the clause or other words that theterm “whereby” modifies do not establish specific further limitations ofthe claim or otherwise restricts the meaning or scope of the claim.

The term “e.g.” and like terms mean “for example”, and thus does notlimit the term or phrase it explains. For example, in a sentence “thecomputer sends data (e.g., instructions, a data structure) over theInternet”, the term “e.g.” explains that “instructions” are an exampleof “data” that the computer may send over the Internet, and alsoexplains that “a data structure” is an example of “data” that thecomputer may send over the Internet. However, both “instructions” and “adata structure” are merely examples of “data”, and other things besides“instructions” and “a data structure” can be “data”.

The term “respective” and like terms mean “taken individually”. Thus iftwo or more things have “respective” characteristics, then each suchthing has its own characteristic, and these characteristics can bedifferent from each other but need not be. For example, the phrase “eachof two machines has a respective function” means that the first suchmachine has a function and the second such machine has a function aswell. The function of the first machine may or may not be the same asthe function of the second machine.

The term “i.e.” and like terms mean “that is”, and thus limits the termor phrase it explains. For example, in the sentence “the computer sendsdata (i.e., instructions) over the Internet”, the term “i.e.” explainsthat “instructions” are the “data” that the computer sends over theInternet.

Any given numerical range shall include whole and fractions of numberswithin the range. For example, the range “1 to 10” shall be interpretedto specifically include whole numbers between 1 and 10 (e.g., 1, 2, 3,4, . . . 9) and non-whole numbers (e.g. 1.1, 1.2, . . . 1.9).

As used herein “premise” refers to any building or structure or environs(interior or exterior) within which there are power draws, for exampleappliances and equipment. In one aspect, a premise is a residence. Inanother aspect, a premise is a commercial building or office or factoryor institution.

As used herein “energy consumption device” should be interpreted broadlyto refer to any device which either draws power or consumes energy.

As used herein “appliance” should be interpreted broadly to refer to anyappliance which draws power within a premise, for example, a device,tools, a fixture (including light fixtures), an apparatus, an electricalsocket etc. . . . As used herein, “power draw” or “drawer of power”refers to both power draw and/or energy consumption. It is to beunderstood that preferably, a sensor may measure, to perform loaddisaggregation on either or both of: power demand and energyconsumption. Most smart grids mainly measure and record the “energyconsumption” even though they are often capable of also measuring “powerdemand”. The unit for power demand is often “kW”, whereas for energyconsumption it's “kWh” (power is to energy as speed is to distance).

In the present disclosure and claims, the word “comprising” and itsderivatives including “comprises” and “comprise” include each of thestated integers but does not exclude the inclusion of one or morefurther integers or elements.

In essence, the present invention provides systems and methods ofproviding granular consumption information to users on “computingplatforms” (including, but not limited to, mobile devices such asSmartphones, tablets, netbooks and laptops, as well as non-mobilepersonal computers). The granular consumption information describedherein can be generated by for example, smart grid networks, or bycustom electric consumption sensors (e.g. current sensors, etc.). Thereare also numerous ways to communicate the generated data to the enduser. For example, this includes the use of the Internet, regionalwireless communication methods, cellular networks, home networks such asWi-Fi, broadband, Zigbee™, etc.

What the present invention provides is a presentation to a user of,rather than raw information, highly functional real time derivatives ofsuch data, which comprise actionable information which can be used tomaximize impact on a user's behavior in regards to power consumption.

It is well understood that Smart Metering technology is available andused today and such raw data produced by Smart Meters can readily beacquired by consumers. The method and system of the present inventionadds value to that raw data and presents it in a highly functional andoften real-time interactive manner to the user. In other words, there isprovided a means: i) to provide to a user, within a premises, greaterpersonal benefit by installation of the Smart Meter and ii) to provide,to utilities and power traders, granular power draw information andconsumption data.

Load Disaggregation (LD)

As used herein, the term “load disaggregation” refers: the analysis ofchanges in the voltage and current going into a premise and deducingwhat devices/appliances are used in the premise as well as theindividual energy consumption of each In literature, LD by definitionmeans not using individual sensors for each device/appliance, but onlylooking at the aggregate consumption of a premise. The present inventionprovides a method by which a user is engaged (to switch a device on andoff, as described herein) in order to a) assist in the LD calculationand 2) avoid the requirement of sensors on each device.

This is important for the following reasons:

1. Using the method and system of the invention, one can disaggregate apremise without having to build up a lot of historic data. That can takeweeks and when a user gets a brand new system, he/she wants to be ableto use it immediately, so that's an important advantage;

2. Using the method and system of the invention additional informationis acquired from the user (e.g., name of appliances they have, isolatedincidents of those appliances being turned on and off, etc.), whichinformation can be extremely helpful for disaggregation;

3. Using the method and system of the invention, the accuracy ofdisaggregation is enhanced, with the collection/compilation of new datapoints (for example, isolated on/off incidents, name of appliances inthe premise);

4. Existing algorithms, at time, may not be able to disaggregateproperly at all because of lack of info such as list of existingappliances in a premise, but by using the method and system of theinvention, it is possible to achieve LD in any premise;

5. The method and system of the invention are deployed with simpleralgorithms and less processor-intensive software. This translates intoefficiency and cost-effectiveness;

6. The method and system of the invention operates in real-time, whichenables many applications for utilities and premise owners.

Load Disaggregation may also be referred to as Nonintrusive LoadMonitoring or NILM.

So, in one aspect of the present invention, there is provided herein amethod of capturing and cataloguing power usage such that it can beascribed to a particular power draw (for example, an appliance). Withoutattaching power sensors onto every single appliance in a home, which isexpensive and cumbersome, it is challenging to make a correlationbetween the raw power usage data and total load into individualappliances. Load disaggregation is assessed by the proprietary methodsprovided herein and therein used to determine the energy consumption ofindividual appliances by monitoring only the power demand of the totalload. One aspect of the present invention is the ability to accuratelyload disaggregate without the need for multiple appliance sensors.

In another aspect, the LD data acquired thereby is applied to themethods and systems of power modeling and forecasting.

There are a variety of LD methods in the literature which attempt toestimate a breakdown of consuming appliances, in real-time or otherwise.Such algorithms may require superior hardware (e.g., higher samplingrates), sophisticated algorithms, a thorough database of all devicepattern signatures, and an adequate computing platform. Alternativemethods use specialized hardware, such as “smart plugs,” to be installedon each appliance so that each appliance's consumption can be measuredseparately.

It is to be understood that within the method and system of the presentinvention, LD requires a smart meter or equivalent sensor device at thepremise to measure an aggregate output signal from the premise but doesnot require appliance specific sensors. Disaggregation is achieved byway of a user directed and managed application, applying the proprietarymethod of the invention, as described herein. It is to be understood;however, that once LD data for a particular consumption device isacquired, catalogued and stored (i.e. a power profile for that device iscreated), additional user input with regard to that device is notrequired. Nonetheless, LD data for that device may be used to isolatepower draws for other devices and to assist in all of the methods ofpredictions and forecasting as provided herein.

Analysis of Aggregate Signal—No LD Required

It is to be understood that not all method and systems of the presentinvention depend upon manual LD but rather, in other aspects, there isprovided an analysis and breakdown of the aggregate signal from apremise. More particularly, this latter aspect of the inventioninvolves. i) receiving said aggregated signal from a sensor; ii)collecting and recording the aggregate signal over a plurality of timeresolutions and frequencies, iii) creating a predicted aggregate signalpattern for each time x and frequency y; vi) detecting changes in thepredicted aggregate signal pattern at time x an frequency y (detectedconsumption pattern changes).

A good illustration of these two independent aspects of the presentinvention (manual LD analysis vs aggregate signal analysis) is asfollows, with a notification system as an example:

user wishes to be notified when she has left some devices ON by accident(ex; a heater left one as user leaves a house)

non-LD dependent method and system of the invention looks at user'stotal home usage (say it reads 1200 watts) and compare that to her homeusage when all unnecessary appliances are off (this is her ‘baseline’consumption, in this example, 150 watts). So if her home reads 1200watts instead of 150 watts while the user is leaving, a device is ON.Using the preferred application of the invention, the user will beimmediately informed (preferably via mobile computing interface) aboutthe reading and what it means.

A sample message might read “You're leaving your home but you haveforgotten to turn everything off. Please go back and double check.”

If an LD or Manual LD protocol was also in place in the above example,the user could have also been advised that “You're leaving your home butyour heater is still ON. Please go back and turn it off.” In otherwords, LD allows specific granular identification of the power consumingdevice.

The present invention provides, in one aspect, a system for acquiringand storing disaggregated power consumption data in a premises whichcomprises:

a) at least one sensor configured to measure at least one desired energyconsumption variable associated with a plurality of energy consumptiondevices within the premises and to generate at least one aggregatedoutput signal therefrom;b) a data processor configured to receive said aggregated signal fromthe sensor; said processor comprising a means to create and update apower profile for each individual device, said data processor comprisinga memory which comprises a catalogue of each of said individual devicesand a respective power draw of each device.

It is preferred that the catalogue comprises a data set acquired by aprocess wherein one or more devices is independently switched betweenon-off, at least one time to isolate a power draw for said device fromthe aggregated signal. It is to be understood that there is norequirement for every single device within a premise to be turned on/offto isolate power draws. For example, a fridge can be easily identifiedwithout the user turning it on/off (which would be hard to do). In afurther preferred embodiment, the catalogue comprises a data setacquired by a set-up process of a sub-set of devices, within a premise,wherein one or more devices with that subset is independently switchedbetween on-off, at least one time to isolate a power draw from theaggregated signal.

With the scope of the invention, the method further comprises the stepof acquiring a “delta” for a device within a premise (i.e. thedifference between its off state and one state). The method furthercomprises, for a device, estimating a delta for a device, usingON-OFF-ON sequences (or OFF-ON-OFF) acquiring a start value and endvalue, and comparing the start value and end value to assess reliabilityof the estimated delta for the device.

Having extracted (and thus isolated) a subset of devices can improve thedisaggregation of other devices as well.

It is preferred that the sensor is selected from the group consisting ofa current sensor, a voltage sensor, a temperature sensor, an activitysensor, and an acoustic sensor. More than one type of sensor may beemployed at a premise.

It is preferred that the system additionally comprises a communicationinterface configured for receiving user commands and queries, forrequesting user input in respect to said devices and for transmittinginformation relating to the devices to the user. More preferably, thecommunication interface is selected from wired and wirelesscommunication technologies. Even more preferably, the communicationinterface is selected from RS232, USB, Firewire™, Ethernet, Zigbee™,Wifi, Bluetooth™, RFJID, wireless USB, cellular, and WMAN communicationtechnologies.

It is preferred that the processor as provided within the present methodand system is configured within a mobile computing device. Morepreferably, the mobile computing device is selected from the groupconsisting of a smartphone, tablet, netbook and laptop computer. Theprocessor as provided within the present system may be configured withinan In-Home Display (IHD) platform or a home-energy management device(for example, some companies are offering their customers tablets forhome control including energy management). Such devices are operablewithin the method and system of the invention.

It is preferred that the sensor is a Smartmeter. It is to be understoodthat this system and method will work with only one smart meter at thepremises.

The present invention provides, in another related aspect, a computerimplemented method of acquiring, cataloguing and storing powerconsumption data in respect to a first energy consumption device (withan energy draw) within a premises comprising a plurality of energyconsumption devices which comprises:

a) providing a sensor configured to measure at least one desired energyconsumption variable associated with the plurality of energy consumptiondevices (including the first device) within the premises and to generateat least one aggregated output signal therefrom;b) configuring a data processor to receive said aggregated signal fromthe sensor;c) creating a power profile for the first device by instructing a user,via a user interface, to independently switch said device between on-offpositions (“switching set up”), at least one time, to isolate a powerdraw for said device from the aggregated signal, wherein data processorrecognizes that the first device was selected and isolates adifferential in the aggregate signal based on differing switch positionsduring the switching set up, said differential being the energy draw ofthe first device; andd) providing a memory which recallably stores the energy draw of thefirst device in a catalogue.

It is preferred that step c) comprises a set-up protocol which isrepeated for a plurality of energy consumption devices in the premisesto create a catalogue of respective energy draws for each device. It isto be understood that the set-up protocol need only be done once foreach device, with thereafter the catalogue comprising the respectiveenergy draws for each device. Furthermore, as noted above, it is to beunderstood that there is no requirement for every single device within apremise to be turned on/off to isolate power draws.

It is preferred that the sensor is selected from a current sensor, avoltage sensor, a temperature sensor, an activity sensor, and anacoustic sensor. It is preferred that the data processor additionallycomprises a communication interface configured for receiving usercommands and queries, for requesting user input in respect to saiddevice and for transmitting information relating to the device to theuser. Such communication interface may be selected from wired andwireless communication technologies. More preferably the communicationinterface is selected from RS232, USB, Firewire™, Ethernet, Zigbee™,Wifi, Bluetooth™, RFJID, wireless USB, cellular, and WMAN communicationtechnologies.

Preferably, the method of the present invention is implemented with asensor which is a Smart Meter. Preferably, at step c) of the methoddescribed above, the device is toggled between on-off positions at theswitching set up more than once.

It is preferred that the user interface employed within the method ofthe present invention provides a graphic representation to the user ofthe differential in power output between the toggled switch positions inrespect to said device. The user interface preferably provides a graphicrepresentation to the user of the differential and additionallycomprises during switching set up, a prompt to the user to toggle thedevice between on-off positions up more than once in response to noisein the graphic representation.

Within the method of the present invention, noise is preferably removedby way of averaging or median calculation of the multiple differentialmeasurements for the device i.e. repeated switching or toggling ofdevice between on and off positions in response to demand by processor,via user interface.

The present invention provides, in yet another aspect, a powerconsumption and notification system comprises:

a) at least one sensor configured to measure at least one desired energyconsumption variable associated with at least one energy consumptiondevice within a premises and to generate at least one aggregated outputsignal therefrom;b) a data processor configured to receive said aggregated signal fromthe sensor; said processor comprising a means to create and update apower profile for each at least said one device, said data processorcomprising a memory which comprises a catalogue of each of at least saidone device and a respective power draw of each such device, said dataprocessor including a means to collect and analyze raw data in realtime, from at least one of following sources: smart grid networks;current sensors; user inputs relating to user-defined budgets; userinputs relating to his behaviors and schedules; user inputs relating tothe function and activities of the devices; other user informationavailable through a networked device such as contacts, demographics,etc; GPS and other location signals such as WiFi network IDs, names andsignal strengths; macrogrid outputs from within a population in whichuser belongs; television and radio signals; and memory based historicalconsumption data, said data processor including means to createcommunications to user based on information acquired from any of thesources; andc) a user interface.

Preferably, real time is within a five minute interval or less.

Within one aspect of the present invention, there is provided anotification system wherein notifications are proactively presented tousers, in a user interface, such notifications being generated by theanalysis of raw data using the system and method of the presentinvention. In one respect, one component of the raw data is acquired bymonitoring and analyzing user behaviors, and informing them of potentialactionable information that presents them with immediate value,including saving potential, safety and security improvement, etc.

Another aspect of the present invention provides a means to engage usersproactively in power measurement and monitoring. It is necessary toensure minimum user effort and investment for harvesting the value ofdata. To require users to actively ‘open’ the application to receivefeedback may be detrimental to that objective. Therefore, in a preferredform, notifications provided to a use, at any given user interface, arebe used to provide the value to users proactively. The notifications aregenerated based on external events or user-configured internalschedules. The notifications may be generated by external processors and‘pushed’ to the computing platform, or it could be the result ofevaluations performed on the computing platform itself.

Applications on Mobile Devices

Mobile devices and networking technologies have transformed manyimportant aspects of everyday life. Mobile devices, such as Smartphones, other cell phones, personal digital assistants, enterprisedigital assistants, tablets and the like, have become a daily necessityrather than a luxury, communication tool, and/or entertainment center,providing individuals with tools to manage and perform work functionssuch as reading and/or writing emails, setting up calendaring eventssuch as meetings, providing games and entertainment aspects, and/orstore records and images in a permanent and reliable medium. Theinternet has provided users with virtually unlimited access to remotesystems, information and associated applications.

As mobile devices and networking technologies have become robust, secureand reliable, ever more consumers are shifting paradigms and employingthese technologies to undertake and create opportunities for meaningfuldata collection and use. It is within the backdrop that the system andmethod of the present invention was developed.

In a preferred aspect of the present invention, a user creates a powerprofile for an energy consumption device (for example an appliance) byway of an application on a mobile processing device which applicationmay be pre-installed on mobile devices during manufacture or can bedownloaded by users/customers from various mobile software distributionplatforms, or web applications delivered over, for example, HTTP whichuse server-side or client-side processing (for example, JavaScript) toprovide an “application-like” experience within a Web browser. Withinthe scope of the present invention, users of mobile processing devicesdownload an application to enable the text/video/audio engagement, asdescribed herein (the “PowerTab™” App).

To install a mobile device application, a user will typically eitherdrag and drop an icon to the device or click a button to agree to theinstallation. Uninstalling one is also straightforward, and typicallyinvolves deleting or dragging the icon away from the device. When a useruninstalls a mobile device application, he or she may also lose all thedata relating to it because, in many cases, it is not stored separately.The number of applications that can be installed on a single phonedepends on the phone's memory.

In another embodiment, the system and method according to the inventionmay be used with a web site operated on a server, accessible over theInternet by users using computer systems, who may upload data, search,view and post content on the web site and have an ability to viewcontent posted on the web site by other users of the application.

The web site is a collection of web pages, hosted on one or moreservers. Users typically connect to web site on the Internet usinghyperlinks, also referred to as links. By clicking on a link, a userdirects a browser operating on computer system to open a window on themonitor of the computer system showing the web site associated with thelink. Typically users must register with web site.

Such a registration system may include obtaining information about theuser such as his/her name, email address, geographic information, suchas address, or country of residence, and the like. Once registered,users can log on to web site using a user name and password, which areprovided by server or selected by the user on registration. The userwill also be provided a personal web page at web site at which they canupload and display content, preferences and their data related tohis/her premise.

Preferably, the server has a database which stores the web site, thecontent thereon, associated web pages, records about each user and thecontent, and information about each link. When a user visits the homepage, they may log in, if they are a registered user. If they are not aregistered user, they may be unable to access certain features of theweb site, but server records the IP address of the unregistered user,and offers the unregistered user an opportunity to register.

While there are likely other smart-grid apps in the market withsmartphone notification features, the system and method of the presentinvention differ in that they preferably provide:

Proactive notifications to provide users with budgeting feedback. Usingalgorithms as provided herein, user's real-time consumption can beevaluated within the objective of a user-defined desired budget, andfeedback could be provided to users to indicate over consumption(negative feedbacks) or achievements (positive feedbacks).

Proactively reminding users if they have accidentally left anappliance/device on, when they leave their house. The feedback to usercan include any or all of the following: a breakdown of the devices lefton by accident, the consequences of it in terms of dollars orenvironmental effects, etc. . . .

Additional data such as a users ‘away’ hours based on their usualconsumption may be determined and such data acquired, stored andanalyzed, based upon, for example, the monitoring of specific triggersin real-time consumption to perceive whether users are about to leave orhave just left home, or by requesting additional information from users,or by considering additional information available on user's computingplatform. This latter includes GPS signals for instance. In particular,one preferred power external signal is the Wi-Fi range and availability,which could accurately estimate user's position with regards to theirhome. All of the above information can be used independently or togetherinside a probabilistic platform to improve detection accuracy.

All of the above analyses may be implemented on a mobile computingplatform, or on a remote server and then pushed to the mobile platform.

A user is informed using the notifications, as he prepares to leave apremises, is about to leave, or has recently left.

A user can be proactively informed of devices he has left on when hegoes to bed. Data is incrementally gathered (on for example, typicalMonday to Friday sleep and waking periods of a user) and it isthereafter possible to learn a user's bedtime behaviors based on theconsumption data, or through data acquired directly or indirectly fromthe mobile platform (e.g. platform being docked or plugged in whichoften occurs at the bed table, an alarm being set, etc.).

Other than excess consumption, leaving devices on by accident could havesafety ramifications. Items such as clothing iron, hair iron, oven, etc.could cause various damages if left on by accident over an extendedperiod. The notification system in this app can be used to inform usersof such mistakes and warm them of possible consequences.

Using home automation systems, the intelligent algorithms used inimplementing the method of the present invention can be used not only toprovide notification to users, but also to act automatically or based onuser response to turn off devices if necessary.

Using home automation systems, the intelligent algorithms used inimplementing the method of the present invention can be used not only toprovide notification to users, but also to provide notifications toother appliances and devices regarding the user behavior: e.g., turn onthe coffee maker when the user wakes up in the morning, or adjustingthermostats as user wakes up, leaves home, or is about to return home

Notifications on a mobile platform could also be used for providingsecurity feedback to users. Unusual changes in consumption when usersare expected to be away could be an indication of intrusion. The usercould be away for work, or away for an extended period for holidays, andcould set this feature as an additional security warning. This, too, canbe presented to users via notification, or it could be provided to themvia text messaging, email, or other forms of communication. It couldalso be used to complement existing security systems by providing themwith additional indicator signals.

The detection of various events similar to ones discussed above could bea source of information that is shared with other applications on thecomputing platform, or sent over the internet to be used for otherservices.

As an alternative to “external notifications”, consumption feedback inaccordance with the present invention can be provided to users usingspaces on any particular interface with which they most frequentlyinteract. This includes a home screen, a lock screen on mobileplatforms, notification bars at the edges of a screen, to name a few.The information within these spaces is made available to usersproactively as they use their computing platform, without requiringusers to open any specific application. Examples include home screenwidgets. Another example is using simple visual cues such as changingthe color of the time or clock text/icon on the screen, to reflect thecurrent electricity rate in Time-of-Use Billing regions. For instance, ahigh-temperature color (e.g. red) on the clock could indicate high costof consumption, or current high consumption by user, or user exceedingbudget, or a combination of those.

Device Interface

FIG. 1 illustrates a graphic representation of a preferred userinterface “front page” 10 presenting easy to read consumption data tousers. A center meter 12 depicts power consumed to date 14, hourly useinformation 16 and budget target pointer 18. The latter may bemanipulated and dynamically updated by user as desired.

Device Profiling

While there are numerous hardware tools to estimate electronic deviceconsumption/cost, the method and system of the present applicationrequires no device-specific hardware and relies solely on granularconsumption data. The method involves monitoring the changes inreal-time consumption rate, and correlating that to specific appliancesusing limited user input, and finally presenting the information to userin actionable and understandable ways.

There are a variety of ‘load-disaggregation’ methods in the literaturewhich attempt to estimate a breakdown of consuming appliances, inreal-time or otherwise. Such algorithms often require superior hardware(e.g., higher sampling rates), sophisticated algorithms, a thoroughdatabase of all device pattern signatures, and an adequate computingplatform. Alternative methods use specialized hardware, such as “smartplugs,” to be installed on each appliance so that each appliance'sconsumption can be measured separately. Attaching smart plugs to alldevices in a premises could cost thousands of dollars.

The value of this work is its ability to accurately estimate individualdevice consumptions with minimum data samples, and by taking advantageof simple and quick inputs from the user. No additional hardware orsophisticated computing is required beyond the one metering sensor. Themethod of the present invention is implemented on mobile platforms toallow users to quickly ‘catalogue’ their household (devices and theenergy draw of each device) using a simple and user friendly means.

A user is provided with a ‘device profiling’ wizard that provides ameans to catalogue some or all devices within a household (also referredto herein generically as a “premises”) by estimating consumption of aset of devices. As used herein, the term “wizard” is a coined term torefer to the combination of at least the processor, interface andmemory, in accordance with the system of this invention The wizard, in apreferred form, requires the user to turn the device switch on at leastonce and in some instances multiple times, to allow the algorithm toobserve the consumption changes caused by the device. Alternatively, thewizard could be triggered automatically when noticeable consumptionchanges are observed, to ask user to identify the source.

The wizard process can also ask users for a limited set of additionalinformation such as device classification, or more detailed informationsuch as timing and length of the periods of usage of the device (e.g.,minutes and hours per day, days per months, etc.).

Consumption rate is monitored continuously in real-time, to observechanges made by individual devices. The consumption data could be takenmultiple times per second, or as few as once every few minutes. The datamay be communicated to the computing platform in real-time or withdelay, one at a time or in bursts.

Referring to FIGS. 2A-F, the sequential steps of device set up areillustrated. An oven is selected as the device to profile in interfacepane of FIG. 2C and the system previous to such selection, had prompteduser to turn the switch of the device at interface pane of FIG. 2A.Between panes of FIGS. 2A and 2D where the use is instructed to turnswitch again, system communicated with sensor and acquired power readingmeasurements from a sensor such that a differential reading of inputbetween steps of FIGS. 2A and 2D could be attributed to the power drawof the oven.

In operation, at FIG. 2A the application (app) wizard prompts the userto turn the device switch for the 1st time. At FIGS. 2B and 2C, the appwizard prompts user to input device information while real-timeconsumption measurements are being taken in the background. At FIG. 2D,the app wizard prompts the user to turn the switch for a 2nd time. AtFIGS. 2E and 2F, the user is requested to confirm device usage infoestimated based on device category, while the app takes final usagemeasurements. Once user inputs the information and final usagemeasurements are taken, user can save the result into his/her catalogue

Referring to FIG. 3, there is depicted a real-time usage graph showingusage cost after user turns a device on for the first time.

Referring to FIG. 4, it can be seen that a user could turn the switchonce or multiple times to provide additional information for improvingaccuracy and reliability of the resulting estimate. The step of turninga device on and off at least once is referred to herein as “switchingset up”. The user may be asked to confirm when the switch is turned, inorder to provide the system with more information to help it identifythe consumption changes of interest, and associate them with theuser-intended device. Alternatively, the system could also monitor anddetect sudden consumption changes, and avoid asking user for flaggingthe timing. This helps simplify the process for user.

There are a number of measures, which can be implemented fully inaccordance with the present invention, to evaluate the confidence andaccuracy in the resulting device consumption estimate. The confidenceresult can be reported to user, or can be used to compensate for theerror, or to discard the unreliable device cost estimation results.

One way to estimate reliability of the a) the information regarding theenergy draw of a selected device and b) the device cost estimation, isby an additionally step of having user to turn device switch more thanonce. Observing multiple triggers helps the system observe consistencyin the measurements, and use averaging to remove noise, etc. Thescenario in which an unknown device is turned on—unintentionally—whilethe device profiling Wizard is in progress, is illustrated in FIG. 5. Asa result, the usage measurements before turning the device on are notthe same as when the device is turned back off again. This inconsistencyshows lack of reliability in the cost estimation results. In accordancewith a preferred method of the present invention, this problem isaddressed by input of multiple triggers during switching set up.

FIG. 6 illustrates another way to evaluate the reliability of such costestimation wherein noise and deviation in the measurement data isevaluated before and/or after the device profiling Wizard process. Noisyenvironment can be reported to user, or the potential accuracy in thedevice cost can be presented, or the Wizard can disable new deviceprofiling process in presence of excessive noise, or finally, the Wizardcan take additional measurement samples in order to compensate for thenoise using noise removal techniques such as averaging or median. Anoisy environment is demonstrated in FIG. 6 in for the same scenariopresented previously.

Statistical formulations can be used to remove noise and outliers in themeasurements. In addition, probabilistic frameworks are used to evaluateexact timings at which the device is triggered and associate that withthe device that the user intended to evaluate.

FIGS. 7A-C illustrate an interface showing the cataloguing ofdevices/appliances wherein FIG. 7A shows a list of all profiled devicesand their consumption; FIG. 7B shows specific social information can bepresented about each device (e.g., how much the community pays onaverage for the energy usage of a similar device) and FIG. 7C shows analternative graphical representation for the catalogued devices.

Cataloguing

Using the aforementioned Device Profiling process, users can create asnapshot of their household consumption by estimating consumptions ofmultiple devices. This is referred to as cataloguing. The result of theDevice Profiling process can be presented to users using understandablegraphical representations such as pie charts, etc. The representationscan demonstrate the breakdown of the current real-time consumption rate,or the breakdown of utility bills, or the breakdown of daily, monthly orannual consumption/spending, etc. Environmental metrics can also beused.

The cataloguing data can be used in a social context, by permittingusers to share info with others. Additionally, the social component canbe used to provide further actionable and understandable feedback tousers by performing comparisons to neighbors, community, and friends.

FIG. 8 illustrates that the cataloguing information can be used forconsumer analytics such as defining and classifying user demographics,modeling user consumption behavior, consumer bill/consumptionforecasting, etc. . . . Home cataloguing can also be used for largescale analytics—utilities, power traders, regulators. For example, thehouse cataloging information and the resulting refined demographicclassification can be used in demand load forecasting and regional usagebreakdowns.

The additional information generated by the user through the homecataloguing process can be used to for target advertisement andcommunication, by retailers, utilities, governing bodies, etc. to offerproduct, services, promotions or education to specific class of userswith clear need or interest.

Social

A social aspect and application of the method and system of the presentinvention involves using information gathered about a user of theapplication to create more value by presenting data in more tangible andactionable ways. The user data is driven from their consumption habits,mobile computing information, or direct inputs by the user in the app.Following use cases showcase the potential applications for this:

Connect users to people in similar regions or demographics, to exchangeinformation on consumption and saving.

Gather user generated content (articles, comments, questions, feedback),as well as professional content, and present them to users based onintelligent targeting strategies (e.g., based on user profile,demographic, consumption, and even home catalogue information). Forinstance, a user with high heating consumption is presented withsuggestions and feedback from other users who successfully reduced theirhigh heating consumption.

Show how other users in one's community, city, demographic or socialcircle consume power (overall or time and device specific), as well ashow and why some do better than others.

By complementing existing popular events such as Earth Day, or byintroducing new similar collective experiences, users are formed into acollective and their affect is made visible and tangible to them usingthe information gathered by the app. The collective includes users ownsocial peers or complete strangers from outside their network orcommunity. The app provides users with a feedback on en-masse movementsto reduce consumption or improve behavior. This could include real-timefeedback as events like Earth-day occur, to demonstrate the en-massesavings and conservation, the environmental impact, etc.

Similar to above,

Connect users to people in similar regions or demographics, to exchangeinformation on consumption and saving.

Gather user generated content (articles, comments, questions, feedback),as well as professional content, and present them to users based onintelligent targeting strategies (e.g., based on user profile,demographic, consumption, and even home catalogue information). Forinstance, a user with high heating consumption is presented withsuggestions and feedback from other users who successfully reduced theirhigh heating consumption.

Show how other users in one's community, city, demographic or socialcircle consume power (overall or time and device specific), as well ashow and why some do better than others.

Furthermore, a basic social application will assist users inshared-living spaces, to create collective conservation objectives, toidentify consumption sources and to split bills.

Aggregated Analysis

In addition to real-time or near real-time user consumption with afrequent sampling interval, a mobile or tablet platform application maycapture additional user information that could be useful for dataanalytics on a larger scale. The information includes—but is not limitedto—name and address, age, sex, location, contacts, etc. Such informationcan be used for creating more accurate demographic profiles and toclassify each user under the appropriate profile. The profiling of userdemographics and the specific user information can be used in additionto the user consumption data, to create more accurate consumption modelsand forecasts, and to provide feedback to third parties such asutilities, power retailers, power traders, etc. All the above data froma sampling of users in a community can be used to create regional andaggregate data analytics for various analytics applications such as loaddemand forecasting, energy theft, etc.

Data Acquisition

New smart meter technology is rapidly being introduced to the industryto facilitate time-of-use metering at residences, permitting utilitiesto charge for electrical usage dependent upon the time of use and forconsumers to take advantage of times at which a lower cost is assessedto the use of electricity. The means to measure at least one desiredenergy consumption variable associated with a plurality of energyconsumption devices within the premises and to generate at least oneaggregated output signal therefrom is preferably be a smart meter.

In one aspect, the system and method may be implemented using a mobilecomputing device which aggregates and analyzes data from a smart meteror other similarly functioning sensor product and enables viewing of thecompiled and enhanced data by a viewer via an interface. In one aspect,the system additionally comprises one or more network managers whichaggregate and relay the data from a data storage system to a server andwherein said server enables viewing of the data by a viewer via aninterface and wherein said interface is selected from the groupconsisting of a desktop computer, a laptop computer, a hand-heldmicroprocessing device, a tablet, a Smartphone, iPhone®, iPad®,PlayBook® and an Android® device. Those skilled in the relevant art willappreciate that the invention can be practiced with many computerconfigurations, including hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, personalcomputers (“PCs”), network PCs, mini-computers, mainframe computers, andthe like. In one aspect, the measurement data is communicated wirelesslyon a peer-to-peer network to a central network manager. In one aspect,the measurement data is collected in situ from network managers orsensors including but not limited to current monitoring sensors that areused to estimate power consumption. This can be achieved by workers onsite either on the ground or using a bucket truck. In one aspect, thesystem comprises more than three sensor nodes. In one aspect, the systemmay be temporarily field deployable on one or more supply lineelectrical wires and then moved and reset on other supply lineelectrical wires without the requirement of any wire splicing for suchdeployment and re-deployment.

Within the scope of the present invention, data acquisition,compilation, and analysis may preferably be controlled by a computer ormicroprocessor. As such, the invention can be implemented in numerousways, including as a process, an apparatus, a system, a computerreadable medium such as a computer readable storage medium or a computernetwork wherein program instructions are sent over optical orcommunication links. In this specification, these implementations, orany other form that the invention may take, may be referred to assystems or techniques. A component such as a processor or a memorydescribed as being configured to perform a task includes both a generalcomponent that is temporarily configured to perform the task at a giventime or a specific component that is manufactured to perform the task.In general, the order of the steps of disclosed processes may be alteredwithin the scope of the invention.

The following discussion provides a brief and general description of asuitable computing environment in which various embodiments of thesystem may be implemented. In particular, this is germane to the networkmanagers, which aggregate measurement data and downstream to the serverswhich enables viewing of the data by a user at an interface.

Although not required, embodiments will be described in the generalcontext of computer-executable instructions, such as programapplications, modules, objects or macros being executed by a computer.Those skilled in the relevant art will appreciate that the invention canbe practiced with other computer configurations, including hand-helddevices, multiprocessor systems, microprocessor-based or programmableconsumer electronics, personal computers (“PCs”), network PCs,mini-computers, mainframe computers, and the like. The embodiments canbe practiced in distributed computing environments where tasks ormodules are performed by remote processing devices, which are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

A computer system may be used as a server including one or moreprocessing units, system memories, and system buses that couple varioussystem components including system memory to a processing unit.Computers will at times be referred to in the singular herein, but thisis not intended to limit the application to a single computing systemsince in typical embodiments, there will be more than one computingsystem or other device involved. Other computer systems may be employed,such as conventional and personal computers, where the size or scale ofthe system allows. The processing unit may be any logic processing unit,such as one or more central processing units (“CPUs”), digital signalprocessors (“DSPs”), application-specific integrated circuits (“ASICs”),etc. Unless described otherwise, the construction and operation of thevarious components are of conventional design. As a result, suchcomponents need not be described in further detail herein, as they willbe understood by those skilled in the relevant art.

A computer system includes a bus, and can employ any known busstructures or architectures, including a memory bus with memorycontroller, a peripheral bus, and a local bus. The computer systemmemory may include read-only memory (“ROM”) and random access memory(“RAM”). A basic input/output system (“BIOS”), which can form part ofthe ROM, contains basic routines that help transfer information betweenelements within the computing system, such as during startup.

The computer system also includes non-volatile memory. The non-volatilememory may take a variety of forms, for example a hard disk drive forreading from and writing to a hard disk, and an optical disk drive and amagnetic disk drive for reading from and writing to removable opticaldisks and magnetic disks, respectively. The optical disk can be aCD-ROM, while the magnetic disk can be a magnetic floppy disk ordiskette. The hard disk drive, optical disk drive and magnetic diskdrive communicate with the processing unit via the system bus. The harddisk drive, optical disk drive and magnetic disk drive may includeappropriate interfaces or controllers coupled between such drives andthe system bus, as is known by those skilled in the relevant art. Thedrives, and their associated computer-readable media, providenon-volatile storage of computer readable instructions, data structures,program modules and other data for the computing system. Although acomputing system may employ hard disks, optical disks and/or magneticdisks, those skilled in the relevant art will appreciate that othertypes of non-volatile computer-readable media that can store dataaccessible by a computer system may be employed, such a magneticcassettes, flash memory cards, digital video disks (“DVD”), Bernoullicartridges, RAMs, ROMs, smart cards, etc.

Various program modules or application programs and/or data can bestored in the computer memory. For example, the system memory may storean operating system, end user application interfaces, serverapplications, and one or more application program interfaces (“APIs”).

The computer system memory also includes one or more networkingapplications, for example a Web server application and/or Web client orbrowser application for permitting the computer to exchange data withsources via the Internet, corporate Intranets, or other networks asdescribed below, as well as with other server applications on servercomputers such as those further discussed below. The networkingapplication in the preferred embodiment is markup language based, suchas hypertext markup language (“HTML”), extensible markup language(“XML”) or wireless markup language (“WML”), and operates with markuplanguages that use syntactically delimited characters added to the dataof a document to represent the structure of the document. A number ofWeb server applications and Web client or browser applications arecommercially available, such those available from Mozilla and Microsoft.

The operating system and various applications/modules and/or data can bestored on the hard disk of the hard disk drive, the optical disk of theoptical disk drive and/or the magnetic disk of the magnetic disk drive.

A computer system can operate in a networked environment using logicalconnections to one or more client computers and/or one or more databasesystems, such as one or more remote computers or networks. A computermay be logically connected to one or more client computers and/ordatabase systems under any known method of permitting computers tocommunicate, for example through a network such as a local area network(“LAN”) and/or a wide area network (“WAN”) including, for example, theInternet. Such networking environments are well known including wiredand wireless enterprise-wide computer networks, intranets, extranets,and the Internet. Other embodiments include other types of communicationnetworks such as telecommunications networks, cellular networks, pagingnetworks, and other mobile networks. The information sent or receivedvia the communications channel may, or may not be encrypted. When usedin a LAN networking environment, a computer is connected to the LANthrough an adapter or network interface card (communicatively linked tothe system bus). When used in a WAN networking environment, a computermay include an interface and modem or other device, such as a networkinterface card, for establishing communications over the WAN/Internet.

In a networked environment, program modules, application programs, ordata, or portions thereof, can be stored in a computer for provision tothe networked computers. In one embodiment, the computer iscommunicatively linked through a network with TCP/IP middle layernetwork protocols; however, other similar network protocol layers areused in other embodiments, such as user datagram protocol (“UDP”). Thoseskilled in the relevant art will readily recognize that these networkconnections are only some examples of establishing communications linksbetween computers, and other links may be used, including wirelesslinks.

While in most instances a computer will operate automatically, where anend user application interface is provided, a user can enter commandsand information into the computer through a user application interfaceincluding input devices, such as a keyboard, and a pointing device, suchas a mouse. Other input devices can include a microphone, joystick,scanner, etc. These and other input devices are connected to theprocessing unit through the user application interface, such as a serialport interface that couples to the system bus, although otherinterfaces, such as a parallel port, a game port, or a wirelessinterface, or a universal serial bus (“USB”) can be used. A monitor orother display device is coupled to the bus via a video interface, suchas a video adapter (not shown). The computer can include other outputdevices, such as speakers, printers, etc.

It is to be understood that the method and system of the presentinvention include not just the aforementioned benefits of simple andinexpensive device disaggregation, power consumption monitoring,user-friendly notifications and power data monitoring. Within anotherkey aspect of the present invention, methods and systems of smartbudgeting are provided. So, the present invention further comprises abudgeting method and system which allows each user to specify a targetbudget for their billing period. The analysis provides users withreal-time feedback as to whether their consumption habits are likely tomeet their desired budget.

The simplest way to approach budgeting is to divide the total budget bythe number of hours in the billing cycle, and inform the user when theirhourly consumption goes beyond the pre-defined hourly budget. However,this method would not provide users with much beneficial feedbackbecause the users require different amounts of electricity at differenthours and days. For instance, if the amount of allocated budget for 5 PMis the same as 5 AM, the user will always appear to be over-consuming(i.e., consumption>budget) at 5 PM, and under-consuming (i.e.,consumption<budget) at 5 AM.

The Smart Budgeting (SB) method and system as described herein, on theother hand, provides users with a more intelligent and practicalfeedback. At each hour, the allocated budget is determined using thefollowing variables:

-   -   a) How much of the budget is left to be consumed (subtract the        money spent so far in the billing period, from the total        budget).    -   b) The forecasted consumption for this day and hour.    -   c) The total forecasted consumption in the remaining portion of        the billing cycle.    -   d) The observed deviation in user's consumption for the current        day and hour.

The Smart Budgeting system and method of the present invention iscapable of taking into account the fact that hours with higherconsumption amount and higher consumption deviation represent betteropportunities for users to conserve energy.

In operation, Smart Budgeting in accordance with the present inventionmay be illustrated (by way of example) as follows:

The given data for analysis:

The closing date of the billing cycle Hourly readings of user'sconsumption over the previous months

Performance Evaluation

Running the algorithm over user's previous months of consumption, thealgorithm performance can be measured by comparing the forecast value tothe actual billing cost of the corresponding period.

$B_{R} = {\sum\limits_{i \in P}C_{i}}$$B_{F} = {\sum\limits_{i \in P}F_{i}}$$e_{p} = {{{B_{F} - B_{R}}} = {{\sum\limits_{i \in P}\left( {F_{i} - C_{i}} \right)}}}$

where C is the hourly consumption, F is the hourly forecast, B_(R) isthe real billing cost, B_(F) is the forecasted bill, P is the billingperiod, and e_(p) is the forecast error of the given period.

Choosing different billing cycle closing dates would result in differenterror values. Herein provided is a performance evaluation method inwhich the outcome depends only on the forecast algorithm itself, and notthe billing period. Hence, the present method uses the above method overall possible billing periods (i.e. starting at every single day in theentire data):

${P\; I} = {\sum\limits_{\forall{P \in {\{ C\}}}} \in_{p}}$

where PI is the Performance Index. The lower the PI, the more accuratethe forecast algorithm.

The PI can be calculated for all available load profiles. Whetherdesigning, improving or comparing forecast algorithms, the ultimateintention is to minimize PI which in turns leads to more accurateforecast bills.

PI can be obtained for different billing cycle lengths (e.g. a weeklong, a month long, or a two month billing cycle). In general, it hasbeen observed that as the billing cycle grows the PI increasesexponentially.

It is worth noting that the forecast made at the beginning of atwo-month billing period is basically the worst case scenario and it islikely to create the most inaccurate result. As the time moves forward,the length of the period over which we forecast shrinks, and the lengthof the time for which actual readings are used grows. Consequently, bythe end of the billing period, the value presented as “forecast bill”consists mostly of actual readings rather than forecast values.Therefore, the accuracy increases as the time passes.

Principles

Based on the examined household load profiles, it can be shown thatthere are very little common behavioral features among differentelectricity users. However, a single user does demonstrate behavioralpatterns over the course of time. The objective of the forecastalgorithm, as applied within the system and method of the invention, isto utilize a pattern recognition system to exploit this fact. Hence, anunsupervised learning approach is suggested based on statisticalanalysis.

Patterns can be found in different frequencies and time-resolutions. Forinstance, a pattern can be found in hours of every day—time-resolutionof an hour, period length of a day (FIG. 9); another example isdetecting a pattern in days of a week—time-resolution of a day, periodlength of a week (FIG. 10).

While multiple patterns can exist simultaneously, the combination ofpatterns varies for different households. For instance, while one usermay demonstrate a very strong hourly behavior every day, another usermay not demonstrate a clear hourly pattern at all. Nevertheless, thesame two users might have strong weekly-day patterns. Consequently, thepresent invention provides a method and system which can analyze allpossible patterns and extract and only the appropriate ones for eachuser.

To make the matter more complicated, on a single frequency, a user mightdemonstrate a behavioral pattern in parts of the period length, and nobehavior at all in the remaining. For instance, most users have a verystrong behavior over sleeping hours (highly repeating, low deviation),but no clear behavior during the daytime (non-repeating, highdeviation). FIG. 9 demonstrates this fact as the deviation in earlyhours of a day is rather minimal, while the deviation of the later hoursof the day varies significantly. Therefore, the forecast algorithmshould be able to integrate the detected patterns in the highesttime-resolution (smallest values for defined below), and for each timeunit in the future use their strongest patterns to make a forecast. Todemonstrate this in FIG. 9 and FIG. 10, the forecast value for 5 AMTuesday should be entirely based on the pattern in FIG. 9; the forecastvalue for 3 PM Thursday should be mostly based on the pattern in FIG.10; and the forecast value for 1 AM Wednesday should take advantages ofthe both patterns.

Pattern Analysis

As noted above, patterns exist in different frequency andtime-resolutions. The consumption data, provided in a resolution, ispresented by C^(α):

C ^(α) ={C ₁ ^(α) ,C ₂ ^(α) ,C ₃ ^(α) , . . . ,C _(N) ^(α)}

The first step, then, is to take this data to the correcttime-resolution for the pattern of interest, β:

$\mspace{20mu} {{{new}\mspace{14mu} {size}\mspace{14mu} N} = \frac{N}{\beta}}$  C^(β) = {C₁^(β), C₂^(β), …  , C_(N)^(β)}$C^{\beta} = {\left. \left\{ {{\sum\limits_{i = 1}^{\frac{\beta}{\alpha}}C_{i}^{\alpha}},{\sum\limits_{i = {\frac{\beta}{\alpha} + 1}}^{2 \cdot \frac{\beta}{\alpha}}C_{i}^{\alpha}},\ldots \mspace{14mu},{\sum\limits_{i = {{{({\overset{.}{N} - 1})} \cdot \frac{\beta}{\alpha}} + 1}}^{\overset{.}{N} \cdot \frac{\beta}{\alpha}}C_{i}^{\alpha}}} \right\}\rightarrow k \right. = {{\left\lbrack {1,\overset{.}{N}} \right\rbrack \text{:}C_{k}^{\beta}} = {\sum\limits_{i = {{{({k - 1})} \cdot \frac{\beta}{\alpha}} + 1}}^{k \cdot \frac{\beta}{\alpha}}C_{i}^{\alpha}}}}$

Note that β≧α, since the desired pattern resolution is never smallerthan the original data's resolution. Next, the mean (μ) is calculatedand the deviation(s) of each β-sized time interval (t), within theperiod length P.

${{{for}\mspace{14mu} t} = {{\left\lbrack {1,\frac{P \cdot \alpha}{\beta}} \right\rbrack \mspace{14mu} {and}\mspace{14mu} d} = {\left\lfloor \frac{\overset{.}{N}}{\frac{P \cdot \alpha}{\beta}} \right\rfloor = \left\lfloor \frac{N}{P \cdot \alpha} \right\rfloor}}},{\mu_{t} = {\frac{1}{d}{\sum\limits_{i = 0}^{d - 1}C_{({{i \cdot d} + t})}^{\beta}}}},{s_{t} = \sqrt{\frac{1}{d - 1}{\sum\limits_{i = 0}^{d - 1}\left( {C_{({{i \cdot d} + t})}^{\beta} - \mu_{t}} \right)^{2}}}}$

A more algorithmic way of representing p and s is:

$\left\{ {{\forall{i\text{:}i\mspace{14mu} \% \frac{P \cdot \alpha}{\beta}}} = {{t\mu_{t}} = {{\frac{1}{d}{\sum\limits_{i}{C_{i}^{\beta} \cdot s_{t}}}} = \sqrt{\frac{1}{d - 1}{\sum\limits_{i}\left( {C_{i}^{\beta} - \mu_{t}} \right)^{2}}}}}} \right\}$

A forecast of the future consumption can be made based on the mean andstandard deviation. While a low standard deviation (s_(t)) indicates ahighly repetitive behavior in the given time resolution and offset, ahigh deviation indicates no significance pattern.

Once the standard deviation is acceptable at the given time-interval tof 1/P frequency, the mean value (μ_(t)) can be used as the predictionof the users future behavior at the same time-interval of futureperiods.

Pattern Analysis Example

The following section demonstrates an example of the above steps. Usingan hourly data provided for a period of a month (N=720 hours), thebehavioral pattern over days of a week are investigated (resolution: 1day or 24 hours, period length P=1 week or 168 hours).

Table 1 shows a portion of the raw data [ref: LM SFD E (ID 2002282), 30Jun. 2006 to 29 Jul. 2006] C^(α), where α=1 hour.

TABLE 1 Raw Consumption Data, Resolution: Hours 0.55 0.53 0.57 0.59 0.540.54 0.55 0.57 1.70 0.86 2.03 1.61 1.52 1.44 5.27 4.82 3.41 4.46 2.234.67 7.07 5.38 3.75 1.56 1.39 1.05 0.65 0.77 0.55 0.58 0.54 2.35 3.911.17 2.70 1.55 1.65 1.38 4.34 3.40 1.29 1.35 1.53 3.79 7.07 3.35 3.911.24 1.50 1.74 0.91 0.92 0.90 0.63 2.75 4.52 4.87 6.13 6.10 4.75 . . .7.04 7.75 6.41 7.27 5.73 5.96 0.88 1.32 0.88 0.57 0.56 0.70 0.84 3.583.62 4.83 1.48 1.28 1.06 1.22 2.14 3.08 1.16 3.60 2.74 4.07 2.45 1.821.82 1.33

Table 2 shows C for resolution β=24 hours:

$\overset{.}{N} = {\frac{N}{\beta} = 30}$

TABLE 2 Consumption Data at 1-Day Resolution [1] Fri [2] Sat [3] Sun [4]Mon [5] Tue [6] Wed [7] Thr [8] Fri [9] Sat [10] Mon 56.186 51.50272.864 81.64 56.909 59.756 57.13 62.92 55.822 42.429 [11] Tue [12] Wed[13] Thr [14] Fri [15] Sat [16] Sun [17] Mon [18] Tue [19] Wed [20] Thr48.701 52.075 57.181 69.254 53.897 41.883 60.563 39.816 55.973 61.194[21] Fri [22] Sat [23] Sun [24] Mon [25] Tue [26] Wed [27] Thr [28] Fri[29] Sat [30] Sun 44.292 54.078 44.725 51.656 37.444 53.175 60.03142.494 72.379 47.006

Finally,

Table 3 presents the values for μ_(t) and s_(t):

${t = {\left\lbrack {1,\frac{P \cdot \alpha}{\beta}} \right\rbrack = \left\lbrack {1,7} \right\rbrack}},{d = {\left\lfloor \frac{N}{P \cdot \alpha} \right\rfloor = 4}}$

TABLE 3 Calculated Mean and Standard Deviation τ μ s 1 (Fri) 53.29 12.52 (Sat) 56.77 4.23 3 (Sun) 48.48 6.34 4 (Mon) 56.88 13.0 5 (Tue) 51.9020.4 6 (Wed) 54.53 2.28 7 (Thr) 59.54 1.69

As shown in previously in FIG. 10, the above load profile demonstrates astrong repeating behavior on Wednesday and Thursdays (s_(wed)=2.28,s_(thr)=1.69), while the behavior on Tuesdays is the least predictive(s_(tue)=2). Therefore, if a prediction is to be made for a comingWednesday, μ_(wed)=54.53 can be used as a reliable estimate.

Trend Analysis

Many behavioral changes occur continuously over the course of time. Anexample of this is shown in FIG. 11. A likely explanation for suchsmooth transitions is the correlation between consumption behavior andseasonal factors such as weather.

When a user's consumption changes, the average-based ‘Pattern Analysis’method would require some time to adjusts its forecasts. This is becausethe new behavior should represent a significant part of thehistory-data, before it shows itself in the mean-values. Therefore, theforecast would lag behind such changes.

In order to decrease the response time, consumption trends can be takeninto account within the present method and system. While PatternAnalysis examines change in consumption over time, Trend Analysisfocuses on the rate of change. As in the above example, the user'sconsumption increase in December is easily predictable in the previousmonth. Hence, detecting trends helps the forecast respond to changesquickly, thus increasing the performance index by minimizing error.

Trends can be examined at different time-resolutions and polynomialorders. Lower time-resolution (large β values) make the trend analysisless sensitive to noise—highly deviated data with insignificantforecasting value. Moreover, higher polynomial orders are moreresponsive to change, but also more sensitive to noise.

After adjusting the consumption data's resolution (same as the initialstep in Pattern Analysis), linear regression is used to detect thetrend:

n: polynomial order,

c=α ₀ :x ^(n)+α₁ ·x ^(n-1)+ . . . +α_(n-1) ·x+α _(n)

where x is the time and c is the consumption. The least-squared solutionto the above polynomial is:m: data points,

$\begin{bmatrix}C_{1} \\C_{2} \\\vdots \\C_{m}\end{bmatrix} = {{{\begin{bmatrix}1 & x_{1} & x_{1}^{2} & \ldots & x_{1}^{n} \\1 & x_{2} & x_{2}^{2} & \ldots & x_{2}^{n} \\\vdots & \vdots & \vdots & \ddots & \vdots \\1 & x_{m} & x_{m}^{2} & \ldots & x_{m}^{n}\end{bmatrix}\begin{bmatrix}a_{0} \\a_{1} \\\ldots \\a_{n}\end{bmatrix}}\mspace{11mu} {- >}Y} = {XA}}$X^(T)C = X^(T)XA   ⇒ A = (X^(T)X)⁻¹X^(T) C

For instance, the solution to a first order polynomial would be:

$\begin{bmatrix}C_{1} \\C_{2} \\\vdots \\C_{m}\end{bmatrix} = {\begin{bmatrix}1 & x_{1} \\1 & x_{2} \\\vdots & \vdots \\1 & x_{m}\end{bmatrix}\begin{bmatrix}a_{0} \\a_{1} \\\ldots \\a_{n}\end{bmatrix}}$

solving for α's:

$\begin{bmatrix}a_{0} \\a_{1}\end{bmatrix} = {{\left( {\begin{bmatrix}1 & 1 & \ldots & 1 \\x_{1} & x_{2} & \ldots & x_{m}\end{bmatrix}\begin{bmatrix}1 & x_{1} \\1 & x_{2} \\\vdots & \vdots \\1 & x_{m}\end{bmatrix}} \right)^{- 1}\mspace{11mu}\begin{bmatrix}1 & 1 & \ldots & 1 \\x_{1} & x_{2} & \ldots & x_{m}\end{bmatrix}}\begin{bmatrix}c_{0} \\c_{1}\end{bmatrix}}$ $n{\sum\limits_{i = 1}^{m}x_{i}}$

Having solved for α's, the polynomial equation can now be used todetermine the consumption at a given time (x) in future.

first order:tr(x)=α₀ ·x+α ₁

n-th order:tr(x)=α₀ ·x ^(n)+α₁ ·x ^(n-1)+ . . . +α_(n-1) ·x+α _(n)

The accuracy of the estimated trend line can be measured by:

${ESS} = {{\sum\limits_{i = 1}^{m}\left( {{{tr}\left( x_{i} \right)} - c_{i}} \right)^{2}} = {{C^{T}C} - {\left( {X^{T}X} \right)^{- 1}X^{T}{CX}^{T}C}}}$

Data Expiry

User consumption behavior changes over the course of time and factorssuch as season can play a significant role in the consumption. Ascollected load data age, they can potentially become less accurate dueto changes in user's life style, season or weather. Therefore, a timewill reach when the data ‘expire’—e.g. the aged data will not beconsidered in analysis any more within the method and system of theinvention.

There are various advantages and disadvantages to eliminating old data:

a. Advantage: the forecast algorithm responds quickly to changes inbehavior.

b. Advantage: less memory is required for storing the aged data.

c. Disadvantage: a temporary change in behavior—a big enough change thatis not sustainable enough for forecasting consideration—cansignificantly affect the forecast (i.e. noise sensitivity).

Each type of pattern or trend calculation can have its own data expirypolicy, since different analysis might require various sizes of historicdata in order to work well.

Forecast Responsiveness

PowerTab™'s forecast algorithm enables forecast responsiveness tochanges in consumption behavior. The method and system of the presentinvention provide a balance between a non-responsive system and a highlyresponsive one. For instance, it is not desirable that the forecastedbill increases vividly when a user's consumption doubles for an entireweek, nor is it desired that the forecast change notably when a user hasdone two hours of laundry.

In long term, non-responsive systems produce more accurate forecasts,while the highly responsive ones occasionally react to noisy data andproduce inaccurate predictions that lowers their overall performance.This is similar to any Control System in which fast response time causesovershoot.

A primary objective of the Smart Budgeting method and system isforecasting the electricity bill based on users current behavior toinform them of how much they will be charged if they continue to consume“this way”. Therefore, rather than trying to forecast with leastpossible error, the forecast value/end product of one aspect of thepresent invention, ties itself greatly with user's current consumptionbehavior. For example, if a user over-consumes for a few days in a row,our system should increases its bill estimation to warn the user abouttheir behavior. If the users over-consumption days are temporary, theincreased estimation introduces error and a non-sensitive forecastsystem can eliminate that error; however, based on the intendedapplication of the forecast system, a consistent over-consumptionbehavior is worthy of warning and hence the error factor is not asimportant as this objective.

Two factors play a role in the forecast responsiveness: trends, and dataexpiry periods. Trends play the most significant role in responsivenesssince they attempt to forecast based on the recent user behaviors (forexample, the last data points in the trend calculation considerablyaffect the trend forecast). This makes the trend analysis very sensitiveto noise—highly deviated data with insignificant forecasting value. Oneway to lessen this over-responsiveness is to use trends onlow-resolution data (large β value such as days or weeks) and hencereducing the noise sensitivity.

Additionally, the length of the data history used for pattern and trendcalculations is another important factor in sensitivity of the forecastsystem. This concept was introduced in the Trend Analysis discussionabove. While the ‘noise sensitivity’ created by Data Expiry can bedisadvantageous to a generic forecast algorithm, it will in fact beadvantageous to the intent of the Smart Budgeting method and system. Thepresent invention does indeed require responsiveness to user's behaviorin order to inform them of the consequences of their current consumptionhabit. And therefore, the only forecasting drawback of the Data Expiryis in fact useful for PowerTab™. The expiry period has to be chosendelicately in order to maintain reasonable responsiveness.

Integration

Various patterns and trends can exist for any given user at any giventime. An important step toward a reliable forecast is integrating allpatterns and trends to obtain a concise outcome. The integration needsto be proportional—a more accurate pattern/trend should affect theoutcome more significantly than a less accurate one. The accuracy of apattern is inversely proportional to s_(t) (standard deviation) at giventime, and the accuracy of a trend is inversely proportional to ESS.

The Smart Budgeting method and system starts with integrating allpatterns first, before applying the trends:

for k patterns and trends,^(u)μ_(x), ^(u)s_(x): mean and standard deviation at time x for patternu^(u)tr(x), ^(v)ESS: trend estimate and error at time x for trend v

$\left\{ {{{\begin{matrix}{{u\text{:}\mspace{14mu} {pattern}},} & {{{}_{}^{}{}_{}^{}} = {\,^{u}\left( \mu_{x} \right)}} \\{u\text{:}\mspace{14mu} {trend}} & {{{}_{}^{}{}_{}^{}} = {{\,^{u}{tr}}(x)}}\end{matrix}{w(x)}} = {\sum\limits_{v = 1}^{k}\frac{1}{{}_{}^{}{}_{}^{}}}},{{P(x)} = {\sum\limits_{u = 1}^{k}\frac{{{}_{}^{}{}_{}^{}} \cdot \frac{1}{{}_{}^{}{}_{}^{}}}{w(x)}}}} \right.$

where w(x) represents the total weight of all pattern forecasts at timex, and f(x) represents the final forecast value. The above methodapplies to patterns/trends of the same time-resolution. Those of varyingresolution can be combined when they are converted to the lowesttime-resolution:

p^(α) = {p₁^(α), p₂^(α), …  , p_(N)^(α)}, α:  resolution, β:  new  resolution, β > α$\left\{ {\left. {\forall{{i\text{:}\mspace{14mu} {\left( {t - 1} \right) \cdot \frac{\beta}{\alpha}}} < x_{i} \leq {t \cdot \frac{\beta}{\alpha}}}} \middle| {{}_{}^{}{}_{}^{}} \right. = {\sum\limits_{i}\; {{}_{}^{}{}_{}^{}}}} \right\}$${P(x)} = {{{}_{}^{}{}_{}^{}} \cdot \frac{{}_{}^{}{}_{}^{}}{{}_{}^{}{}_{t\text{:}\mspace{14mu} \left( {x \in t} \right)}^{}}}$${\overset{\prime}{S}}_{x}^{\alpha} = {{{}_{}^{}{}_{}^{}} \cdot \frac{{}_{}^{}{}_{}^{}}{{}_{}^{}{}_{t\text{:}\mspace{14mu} \left( {x \in t} \right)}^{}}}$

The following steps should be taken to integrate all patterns:

-   -   Integrate all patterns of the highest resolution. Since trends        are only used at lower resolution, no trend would be integrated        at this step.    -   Integrate all patterns/trends of the next highest resolution.    -   Use the technique for varying resolutions to integrate the last        two outcomes.    -   Repeat steps b and c until no lower resolution pattern exists.

Since user behaviors vary diversely, not every pattern or trend analysiscan highlight a useful repeating behavior. However, using the aboveintegration approach, many patterns and trends proportional to theirforecasting strength can be integrated, and in a dynamic, time-efficientmanner.

If after further examination of user behaviors it is discovered that asubstantially small group of users has a very distinct yet strongbehavioral pattern, an appropriate pattern analysis component can beadded to the method and system for those users. This addition wouldstrongly improve forecasting performance for those niche users, whilenot at all degrading the performance for all other users who do notbehave that way. This feature of the present “integration approach”makes the method and system very sustainable for future research andcustomization to new markets.

Examination

Using the load profile data of 17 households over a course of a year,the above principles were adopted to PowerTab™'s specifications. Twopatterns (daily-hours and weekly-days) and one trend (first-orderweekly-based) were found sufficient for an accurate forecastingcapability.

Pattern: Daily-Hours

The highest possible time-resolution of a forecast is equal to thehighest time-resolution of the analyzed patterns. Therefore, to be ableto make hourly forecasts, patterns of hourly behavior were preferablyanalyzed. Clearly, the most useful hourly-based pattern can becalculated for a period-length of one day—hence the name Daily-Hours.Daily-Hours analysis has proved itself very helpful for forecasting,because time of day is one of the most significant parameters for user'sbehavior.

FIG. 12 demonstrates the daily-hour behavior of various users:

A considerable majority of users have a very low-deviating behavior oversleeping hours. Yet, the behavior during the daytime varies. FIG. 1ademonstrates this fact as the deviation in early hours of a day israther minimal, while the deviation of the later hours of the day variessignificantly.

The data-expiry limit for daily-hour analysis is set to 30 days (i.e.data older than 30 days are not used for this analysis). The 30 dayslimit is set in order to keep the algorithm responsive to changes indaily behavior, while making sure it is not too sensitive to noise andoutliers.

Pattern: Weekly-Days

Useful behaviors can be found by analyzing user's daily consumptionduring each week—hence the name Weekly-Days. Factors such as weekdaysand weekends can influence user's consumption behavior; additionally,weekly working schedules of repeating nature are very common. Therefore,as expected, pattern analysis at time-resolution of one day andperiod-length of one week has improved the performance as seen in FIG.13.

The data-expiry limit for weekly-day analysis is set to 60 days (i.e.data older than 60 days are not used for this analysis). This limitprovides an average of 8 samples for each day of the week, which israther minimal for an accurate averaging. Meanwhile, extending thedata-expiry beyond 60 days is dangerous because after two month, thosedata can be obsolete for forecasting purposes (i.e. high possibilitythat user's consumption behavior has changed significantly).

Trend: First-Order Weekly-Based

Based on data acquired, a trend line on a weekly time-resolution andusing a first-order polynomial fitting has proved itself very useful foraccurate forecasting. Any time-resolution higher than one week is proneto frequent error due to noise and outliers. Moreover, 1st order, 2ndorder and 3rd order polynomials were experimented with. While 2nd and3rd order perform better estimates at various occasions, the overallperformance of the 1st order regression was better.

The data-expiry limit for the weekly trend analysis is set to 60 days(i.e. data older than 60 days are not used for this analysis). Thislimit provides 8 data points (weeks) for trend-line calculation. This israther minimal for an accurate trend estimation. Meanwhile, extendingthe data-expiry beyond 60 days is dangerous because after two month,those data can be obsolete for forecasting purposes (i.e. highpossibility that user's consumption behavior has changed significantly).

Importantly, it was discovered that the weekly-based trend analysiscreates a suitable responsiveness for the forecast algorithm. As soon asa user spends a good portion of a week (3 days or more) over-consuming,the weekly consumption for the most recent week increases, causing thetrend-line to shift upward. This effect increases the forecast estimateof the upcoming days. The increase helps warn users about the value oftheir next bill, if they continue their recent consistentover-consumption behavior.

Absence Detection

Two types of patterns exist: repeating, and non-repeating. Repeatingpatterns are useful to forecasting (e.g. sleeping hours), whilenon-repeating patterns—statistical outliers—are misleading. Outliers areinfrequent in nature, and since our approach is based on averaging, theyare insignificant to the outcome.

Behavioral outliers exist as well—non-repeating behavioral patterns thatdeviate from standard. However, as opposed to statistical outliers,behavioral outliers are not always infrequent. Vacation periods are aperfect example of non-infrequent behavioral outliers. Due to theirlength in time, these behavioral outliers can affect the forecastoutcome significantly. For instance, a three-week vacation period cancompletely mislead the forecast algorithm's expectation of the user'sbehavior.

In this case, an absence detection mechanism is implemented with theSmart Budgeting method and system of the present invention whichexcludes from the forecast algorithm, the periods in which no user is athome. One can easily spot absence periods when looking at theconsumption graphs. That is due to human brain's highly capable patternrecognition skills. Absence periods share two characteristics: first,the usage is observably lower than typical consumption periods; thisproperty, however, does not help detecting absence times since both themagnitude of consumption, and the ratio of absence consumption totypical consumption, are rather hard to define as they vary from oneuser to another.

The second characteristic of an absence period is its low deviation inthe consumption record; since no person is present at home, the changesoccurred in the consumption are significantly smaller than that of atypical period. Some time-varying electric appliances such as thermostatheater or air conditioner, however, can introduce deviations to thepower consumption during absence periods. Two solutions may beintroduced to the Smart Budgeting method and system, and when combined,they can solve this problem:

Defining consumption deviation tolerance ranges based on a percentage ofthe typical consumption deviation. If deviation is higher, someone ispresent; if it is lower, no one is.

Use of the previous day's absence status: If a user was absentyesterday, s/he might be on vacation and hence s/he is more likely to beabsent today.

Utilizing fuzzy logic, this two decision methods can be combined basedon the following table:

TABLE 4 Fuzzy Logic Table for Absence Detectior YESTERDAY Yes LikelyUnlikely No TODAY Yes True True True False Likely True True False FalseUnlikely True False False False No True False False FalseThe four fuzzy sets of “Yes,” “Likely,” “Unlikely” and “No” are definedas in FIG. 14.

Finally, to calculate the values for “today” and “tomorrow” variables,we use the following set of equations:

${\mu = {\frac{1}{24}{\sum\limits_{i \in P}\; C_{i}}}},{s = \sqrt{\frac{1}{23}{\sum\limits_{i \in P}\; \left( {C_{i} - \mu_{day}} \right)^{2}}}}$s _(today) =s(P=today's consumption data)

S _(yesterday) =s(P=yesterday's consumption data)

s _(all) =s(P=entire consumption data)

${{today} = \frac{s_{today}}{s_{all}}},{{yesterday} = \frac{s_{yesterday}}{s_{all}}}$

To elaborate on the above equations, the ratio of the today andyesterday's hourly standard deviation, over the overall hourly standarddeviation. If the ratio value is reasonably small in both days, thatshows a lower than usual daily consumption deviation, which helps detectabsence of users.

Light Indicator

Among the most important features of the PowerTab™ is its lightindicator that provides instantaneous feedback to user's consumptionbehavior to help them lower their consumption within their targetedbudget. The light indicator has two states: Red representingoverconsumption, and Green representing proper consumption. When thelight is red, the user is expected to take measures to lower theirconsumption; and when the light is green, the user is notified thattheir current behavior would achieve the target.

Budgeting

The light indicator uses the user-inputted target bill value, the dollarconsumption so far, and the user's forecasted behavior in order todetermine an hourly budget for the remaining part of the billing cycle.The operation is performed as follows:

given: tεQ, Q: remaining period in the current billing cycle

T: target budget ($), S: spent so-far ($), g _(t) cost of 1 kwh at timet

f _(t): forecasted consumption (kwh) at time t, s _(t): forecastsdeviation at time t

First, within one embodiment of the Smart Budgeting method and system,the remaining dollars to be spend during the remaining days of thecurrent billing cycle is calculated:

R: remaining budget ($), R=T−S

Next, a budget for every remaining hour of the billing cycle, based onboth the forecasted spending and its possible deviation, is specified:

Case 1) If the unconsumed budget is more than forecasted spending: theextra money will be divided between all remaining hours, proportional tothe forecast deviation. For instance, since the deviation is smallduring sleeping hours, not much of the extra money will be devoted tothose hours since the user clearly does not need much room there.However, during hours where the user does not spend consistently, he/shewill be given additional budget.

Case 2) If there is some money left in the budget (unconsumed budget>0),yet the left-over is less than the forecasted consumption: this meansthat the user is over-consuming, so his/her hourly forecastedconsumption should be reduced. When giving extra money to each hour,this was allocated proportionally to each hour's consumption deviation.However, when shrinking the consumption, the method and system of thepresent invention does it proportional to the forecasted consumptionitself. That is because one end goal of Smart Budgeting is to encouragethe user to adopt a more conservative behavior by saving at all times.Even during sleeping hours when the deviation is low, turning off anextra appliance might be the key in achieving the target bill, andtherefore he/she is asked to lower every hour of consumption by acertain percentage rather than considering the deviation patterns.

Case 3) Finally, if the amount of money spent so far is more than thetotal budget (remaining budget<0), then the user cannot achieve his/hergoal and a $0 budget for every remaining hour is specified.

The above policies are implemented within the Smart Budgeting method andsystem and represented in the following equations:

F: forecasted spending ($),

${F = {\sum\limits_{t \in Q}\; {f_{t} \cdot g_{t}}}},{{R > {0 \cdot R} \geq {F\text{:}\mspace{14mu} B_{t}}} = {{f_{t} \cdot g_{t}} + {\rho \cdot \frac{s_{t}}{\sum\limits_{u \in Q}\; s_{u}}}}},{{{where}\mspace{14mu} \rho} = {R - F}}$${R > {0 \cdot R} < {F\text{:}\mspace{14mu} B_{t}}} = {{f_{t} \cdot g_{t}} \times \frac{R}{F}}$

State Determination

Once the consumption budget of the remaining billing period isdetermined, the light indicator should decide whether the user isover-consuming (red or green light). The most important criterion forstate determination is whether the consumption of this hour is less thanor equal to this hour's budget:

${rule}\mspace{14mu} {\# 1}\text{:}\mspace{14mu} \left\{ \begin{matrix}{{under}\text{-}{consumption}\text{:}} & {{C_{now} \cdot g_{now}} \leq B_{now}} \\{{over}\text{-}{consumption}\text{:}} & {{C_{now} \cdot g_{now}} > B_{now}}\end{matrix} \right.$

Considering the following scenario: a user's budget is $70. It is the6th week of the 8-week long billing period, and she has spent $30 sofar. Therefore, the user has 2 weeks left and $40 to spare, which meansher consumption can triple and still the target budget will be met. Ifthe user decides to do 3 hours of doing laundry, cooking dinner, ironingand watching TV all at the same time, she will surpass her hourlybudget. Should she be warned about this?

A reasonable answer to this question is ‘no’, because the user has aconsiderable amount of budget left and it is very clear that a mere 3hour of overconsumption would not challenge the achievability of hertarget bill, due to her fine record of under-consumption. However, ifthe above rule were to be considered independently, the light indicatorwould go red which would come as a surprise to the user. Suchunreasonable judgment by PowerTab™ can seriously challenge itstrustworthiness for helping users conserve, which is its primarilyobjective.

To rectify the above problem, a new criterion is introduced within themethod and system of the invention which adds a ‘consistency’ factor tothe decision of whether a user is over-consuming. In other words, notonly the user should be consuming more than the current hour's budget,she should be consistence in it for a short period to receive a warning.To do so the light indicator looks into user's last 24 hours ofconsumption, and if there is a left-over budget within this time, ituses that to tolerate the current over-consumption:

${rule}\mspace{14mu} {\# 2}\text{:}\mspace{14mu} \left\{ \begin{matrix}{{under}\text{-}{consumption}} & {{\sum\limits_{t \in {{last}\mspace{14mu} 24\mspace{14mu} {hours}}}\; {C_{t} \cdot g_{t}}} \leq {\sum\limits_{t \in {{last}\mspace{14mu} 24\mspace{14mu} {hours}}}\; B_{t}}} \\{{over}\text{-}{consumption}} & {{\sum\limits_{t \in {{last}\mspace{14mu} 24\mspace{14mu} {hours}}}\; {C_{t} \cdot g_{t}}} > {\sum\limits_{t \in {{last}\mspace{14mu} 24\mspace{14mu} {hours}}}\; B_{t}}}\end{matrix} \right.$

The light indicator state will be determined based on the rule #1 andrule #2, as follows:

${state}\text{:}\mspace{14mu} \left\{ \begin{matrix}{{red}\text{:}} & {\left( {{{role}\mspace{14mu} {\# 1}} = {OC}} \right) \cdot \left( {{{role}\mspace{14mu} {\# 2}} = {OC}} \right)} \\{{green}\text{:}} & {otherwise}\end{matrix} \right.$

Based on the above system, few hours of over-consumption can betolerated if the user's overall behavior is conservative enough. Thefollowing scenarios elaborate the capabilities of the Smart Budgetingmethod and system:

Case A: A user's budget for the last 24-hour period was $2.4 and hiscurrent hour's budget is $0.12. He has consumed $2.1 during this day. Ifhe consumes more than $0.12 this hour, rule #1 would indicateoverconsumption; however, rule #2 would not, and therefore the lightwill be green. However, this tolerance will be exhausted as soon as theuser consumes anything more than $0.3 within this hour (excessiveoverconsumption).

Case B: If the above user has spent $3 within the last day, rule #2would indicate overconsumption, even if the user is spending less thanhis $0.12 budget for current hour. However, the light would still showgreen, because the extra consumption has already been deducted fromuser's future budgets (hence shrinking the $0.12 slightly). Thus, if theuser is currently spending $0.06, he would correctly see a green lightindicating that if he continues to do what he is doing ‘right now’, hewould be achieving his consumption goal. But as soon as he passes thehourly budget, he would see a red-light since there is no tolerance tofurther overconsumption.

Case C: If a user has a $100 budget, and he has only spent $30 sevenweeks into the period, he would have a $10 per day budget for theremaining days. If he spends $3 in 23 hours, he would still have $7 tospare in one hour which means he would not get an overconsumption alarmthat easily (i.e. very high tolerance).

Dependency on Forecast Algorithm

As demonstrated herein, the forecast value for each hour is the base forits budget determination. An alternative is to use no forecasting andevenly divide the remaining dollars of the budget over every hour. Auser's hourly behavior is not even; in some hours the consumption is low(e.g. when sleeping) and in some hours the consumption is high (e.g.evenings). However, if every hour is budgeted evenly, the system wouldalways indicate a green light during sleeping hours, even if the userhas forgotten to turn off the TV; and it would always show red lightduring evenings, even if the user has consumed less than usual.

The other side of the extreme is if it is known ‘exactly’ how the useris going to behave. In that case, the user would never see a red lightif his future behavior would be meeting the budget requirement, evenwhen he is consuming excessively for a few hours—since his behavior andexcessive consumption is known and expected, it is known that it wouldnot cause him to go over the budget. However, if the user's behaviorleads to surpassing the budget, the system would shrink every futurehour's consumption by a needed percentage to meet the budget. It wouldthen use the light indicator to encourage the user to follow withinthose defined limits. If it is the beginning of the billing cycle, theuser would see green light every now and then. However, if the end ofthe cycle is approaching and the user is still over-consuming, thesaving percentage grows higher and higher and it would become almostimpossible for the user to lower his consumption to that extend.Therefore, no green lights will be shown anymore indicating that it isnot very likely for the user to meet his intended budget.

This is the ideal case, because the light indicator's purpose to answerthe following question is met perfectly: is the user going to go overhis budget? The more accurately the user's behavior can be forecastedusing the Smart Budgeting method and system, the less ‘false positives’and ‘false negatives’ would show.

Using the same principles used in forecasting, absence detection andlight indication, the following features can be implemented asadditional embodiments of the Smart Budgeting method and system:

Sleep-Prep: The PowerTab™ can determine the usual sleeping hours andpatterns of a user. Hence, during the hours in which the user usuallygoes to bed, an icon can appear on the screen showing whether thehousehold is ‘sleep ready’. The PowerTab™ determines the sleep-readinessby analyzing a user's usual sleeping pattern and his budget for thosehours. If the current consumption is similar to that of the user's usualsleeping hours and he is within the budget, the PowerTab™ provides apositive feedback. However, if an extra light is left on, a warning canbe displayed to notify the user.

Leave-Prep: The same idea as the ‘sleep-prep’ can be applied for whenthe user is leaving home especially for longer periods (e.g. vacations).In this case, the user might need to press a button on the PowerTab™ toask for verification that the house is ‘at rest’ (minimum powerconsumption). The PowerTab™ then analyzes previous absence patterns todetermine whether an unnecessary appliance is left on or if house is“leave ready”.

Today's Performance: A simple addition to PowerTab™ can provide afeedback about user's every day performance rather than that of theentire billing cycle. The system can interpolate today's consumptionover the remaining days of the billing cycle and generate a forecastbased on today's performance. This forecast would vary greatly from oneday to another and is not to be trusted as the final bill's value.However, it can help user understand how he has performed today. Also,this would make the device interactive as the users will have adaily-based challenge; they can set new ‘records’ by trying to lowerthan number, and they would not need to wait longer before seeing theeffect of their effort. Users may also use social media to share suchdata and “compete” with neighbors based on performance indicators andother power usage metrics.

Absence Battery Saving: When the forecast algorithm detects that theuser is absent, it can turn PowerTab™ off in order to save battery. Assoon as someone comes back, the system can detect that by observing thesudden jump in the consumption (turning on lights, etc.) and the devicecan turn itself on again.

Sleeping-Hours Battery Saving: The same absence battery saving ideaapplies to the sleeping hours. Again the device can forecast sleepinghours, detect it when a user goes to bed, and turn itself off untilchanges in the consumption indicate the user's awakeness. The PowerTab™can automatically turn its display on during morning hours when the userwakes up because it is likely for the user to pass by.

Interfacing with Appliances: Provision and conveyance of informationabout user's daily behavior to other appliances. Using the PowerTab™,all appliances can be triggered when user sleeps, wakes up, leaves home,goes on vacation, etc. . . . and all this information is detected by thePowerTab™ without any user interaction

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of examples. Insofar as suchexamples contain one or more functions and/or operations, it will beunderstood by those skilled in the art that each function and/oroperation within such examples can be implemented, individually and/orcollectively, by a wide range of hardware, software, firmware, orvirtually any combination thereof. In one embodiment, the presentsubject matter may be implemented via ASICs. However, those skilled inthe art will recognize that the embodiments disclosed herein, in wholeor in part, can be equivalently implemented in standard integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more controllers(e.g., microcontrollers) as one or more programs running on one or moreprocessors (e.g., microprocessors), as firmware, or as virtually anycombination thereof, and that designing the circuitry and/or writing thecode for the software and or firmware would be well within the skill ofone of ordinary skill in the art in light of this disclosure.

In addition, those skilled in the art will appreciate that themechanisms taught herein are capable of being distributed as a programproduct in a variety of forms, and that an illustrative embodimentapplies equally regardless of the particular type of signal bearingmedia used to actually carry out the distribution. Examples of signalbearing media include, but are not limited to, the following: recordabletype media such as floppy disks, hard disk drives, CD ROMs, digitaltape, flash drives and computer memory; and transmission type media suchas digital and analog communication links using TDM or IP basedcommunication links (e.g., packet links).

While the forms of method and system described herein constitutepreferred embodiments of this invention, it is to be understood that theinvention is not limited to these precise forms. As will be apparent tothose skilled in the art, the various embodiments described above can becombined to provide further embodiments. Aspects of the present systems,methods and data collection means (including specific componentsthereof) can be modified, if necessary, to best employ the systems,methods, nodes and components and concepts of the invention. Theseaspects are considered fully within the scope of the invention asclaimed. For example, the various methods described above may omit someacts, include other acts, and/or execute acts in a different order thanset out in the illustrated embodiments.

Further, in the methods taught herein, the various acts may be performedin a different order than that illustrated and described. Additionally,the methods can omit some acts, and/or employ additional acts.

These and other changes can be made to the present systems, methods andarticles in light of the above description. In general, in the followingclaims, the terms used should not be construed to limit the invention tothe specific embodiments disclosed in the specification and the claims,but should be construed to include all possible embodiments along withthe full scope of equivalents to which such claims are entitled.Accordingly, the invention is not limited by the disclosure, but insteadits scope is to be determined entirely by the following claims.

Example User of Power Disaggregation Mobile Application

This example references FIG. 15A-E. User Installs mobile application(PowerTab™) on SmartPhone and is invited via graphical user interface(GUI) indicated generally at 99 (FIG. 15A) on welcome screen 100 tocreate a profile at 102. User inputs username 103, password 104, someinformation about himself and his house and billing period etc. . . .106

User is then presented with a friendly animated light-bulb 108 (FIG.15B). Above it, he sees his current household consumption in real-time,updated once every two seconds at interface segment 110. Below the bulb,he notices he can set a goal for his electricity budget over the currentbilling period at interface segment 112.

The progress bar 114 underneath the bulb also indicates how far he isinto his billing cycle, and under the bar he can see how much he hasspent on electricity so far in this cycle, and what the app estimateshis final bill to cost.

The light bulb itself presents itself with different emotions: happy ifthe bill estimate is within the specified budget 116, concerned if thebill may to exceed the budget, and upset if the budget cannot be met118. The real-time consumption bar on the top of the interface page alsopresents three colors associated with the active Time-of-Use rate. Witha single glance, user knows whether he is at the lowest rate (green), atthe medium rate (yellow) or at the peak rate (red).

Like many users, this user is curious to see what the app shows when heturns a light on and off. This could also help him understand the impactof that light on his current consumption rate.

The app, detecting user's curiosity, prompts him at 120 (FIG. 15C) totry out other appliances as well, and provides him at 122 with a list ofmajor appliances with highest impact on a bill: heaters, dryer, washingmachine, oven, fridge, etc.

As user is guided through the process of profiling his major appliances,the app is recording the consumptions and by the end of the process,user is presented at 122 with a list of his major energy consumers.

User can see at interface segment 126 how much each appliancecontributes to the overall cost of a bill, in kWh, $ as well as abreak-down percentage. If a particular appliance is over consuming, thismay indicate excessive use, poor appliance energy efficiency, or thepossibility of a broken appliance. User is presented at 128 with awarning immediately that his heater may be broken. He proceeds toreplace the heater later and observes a $30 monthly saving on his bill.

The app also presents user at segment 130 with a list of alternativeenergy-efficient appliances offered by third-party retailers, and ratesthe Saving Value of each app based on their Return-on-Investment.

User browses to the Bills page, represented at FIG. 15D. User is askedat 132 to enter the value of his most recent bills, in order to createcomparative bases for the app to estimate his savings from now on. TheBills interface page also presents a historic graph at 134 of houseconsumption, so user can observe his usage over weekdays versusweekends, summer months versus winter months, etc. The graphs can bedisplayed on daily, weekly, or monthly bases for the period of time inwhich a smart meter or an energy sensor has been installed in the house.

User can also glance at interface segment 136, at his previous bills 138and the current bill estimate 140, and see how much savings (the impact)he has accumulated since the start of the app, as well as for everyindividual bill at 142.

He also sees the amount of greenhouse gases the savings translates to,as well as other interesting facts and matrices regarding his energyconsumption performance.

That night as user goes to bed, his PowerTab™ app informs him, viainterface segment 144 that his TV may still be on. If true, user mayelect to turn off that device. The app also notifies him in suggestionbox 146 that if he turns off his bedroom heater and starts using ablanket heater instead, he could use up to $30 per month. The nextmorning, just as user leave home for work, the app informs them, viainterface segment 148 (FIG. 15E) that his iron has been left on. Thiswarning, simple, automatic and directly to user's Smartphone not onlyhelps him cut down on unwanted energy usage, it also notifies him of apotential hazard.

On specific occasions, as programmed by the user or as defined withinthe PowerTab™ app itself, user may be advised by interface message, textor email of such an occasion. One example of an occasion is Earth Day.When user opens the app, he is prompted to participate in saving energyby turning off his lights between 7 pm and 8 pm. If he agrees toparticipate, he joins many other participants across the world.

As the event unfolds, user watches an interactive map 150 in the appthat shows the participants, how much they have all saved so far, andhow much his country, his city and his neighbors are contributing tothis movement.

The present invention provides: A system for acquiring and storingdisaggregated power consumption data in a premise which comprises:

-   -   a) at least one sensor configured to measure at least one        desired energy consumption variable associated with a plurality        of energy consumption devices within the premises and to        generate at least one aggregated output signal therefrom;    -   b) a data processor configured to receive said aggregated signal        from the sensor; said processor comprising a means to create and        update a power profile for each individual device, said data        processor comprising a memory which comprises a catalogue of        each of said individual devices and a respective power draw of        each device.

Preferably, the catalogue comprises a data set acquired by a set-upprotocol wherein a device is independently switched between on-off, atleast one time to isolate a power draw for said device from theaggregated signal. Preferably, the sensor is selected from a currentsensor, a voltage sensor, a temperature sensor, an activity sensor, andan acoustic sensor. Preferably, the system additionally comprises acommunication interface configured for receiving user commands andqueries, for requesting user input in respect to said devices and fortransmitting information relating to the devices to the user.Preferably, the communication interface is selected from wired andwireless communication technologies. Preferably, the communicationinterface is selected from RS232, USB, Firewire™, Ethernet, Zigbee™,Wifi, Bluetooth™, RFJID, wireless USB, cellular, and WMAN communicationtechnologies. Preferably, the processor is configured within a mobilecomputing device. Preferably, the processor is configured within amobile computing device selected from the group consisting of aSmartphone, tablet, netbook and laptop, an In-Home Display (IHD)platform and a home-energy management device. Preferably, the sensor isa smart meter. Preferably, the sensor is a smart meter and only one ispresent in the premises.

A computer implemented method of acquiring, cataloguing and storingpower consumption data in respect to a first energy consumption device(with an energy draw) within a premises comprises a plurality of energyconsumption devices which comprises:

-   -   a) providing a sensor configured to measure at least one desired        energy consumption variable associated with the plurality of        energy consumption devices (including the first device) within        the premises and to generate at least one aggregated output        signal therefrom;    -   b) configuring a data processor to receive said aggregated        signal from the sensor;    -   c) creating a power profile for the first device by instructing        a user, via a user interface, to independently switch said        device between on-off positions (“switching set up”), at least        one time, to isolate a power draw for said device from the        aggregated signal, wherein data processor recognizes that the        first device was selected and isolates a differential in the        aggregate signal based on differing switch positions during the        switching set up, said differential being the energy draw of the        first device; and    -   d) providing a memory which recallably stores the energy draw of        the first device in a catalogue.

Preferably step c) comprises a set-up protocol which is repeated for aplurality of energy consumption devices in the premises to create acatalogue of energy draws for each device and to create an aggregateprofile for the premises. Preferably, the set-up protocol need only bedone once for each device, with thereafter the catalogue comprising therespective energy draws for each device. Preferably, there are aplurality of devices in the premise and only a selection of devices areprofiled. Preferably, the sensor is selected from a current sensor, avoltage sensor, a temperature sensor, an activity sensor, and anacoustic sensor. Preferably, the data processor additionally comprises acommunication interface configured for receiving user commands andqueries, for requesting user input in respect to said device and fortransmitting information relating to the device to the user. Preferably,the communication interface is selected from wired and wirelesscommunication technologies. Preferably, the communication interface isselected from RS232, USB, Firewire™, Ethernet, Zigbee™, Wifi,Bluetooth™, RFJID, wireless USB, cellular, and WMAN communicationtechnologies. Preferably, the processor is configured within a mobilecomputing device. Preferably, the processor is configured within amobile computing device selected from the group consisting of aSmartphone, tablet, netbook and laptop computer, an In-Home Display(IHD) platform and a home-energy management device.

Preferably, the sensor is a smart meter. Preferably, the sensor is asmart meter and is only one is present in the premises. Preferably, atstep c), device is toggled between on-off or off-on positions at theswitching set up more than once. Preferably the user interface providesa graphic representation to the user of the differential. Preferably,the user interface provides a graphic representation to the user of thedifferential and additionally comprises during switching set up, aprompt to the user to toggle the device between on-off positions up morethan once in response to noise in the graphic representation.Preferably, the noise is removed by way of averaging or mediancalculation of the multiple differential measurements.

A power consumption and notification system comprises:

-   -   a) at least one sensor configured to measure at least one        desired energy consumption variable associated with at least one        energy consumption device within a premises and to generate at        least one aggregated output signal therefrom;    -   b) a data processor configured to receive said aggregated signal        from the sensor; said processor comprising a means to create and        update a power profile for each at least said one device, said        data processor comprising a memory which comprises a catalogue        of each of at least said one device and a respective power draw        of each such device, said data processor including a means to        collect and analyze raw data in real time, from at least one of        following sources: Smart grid networks; current sensors; user        inputs relating to user-defined budgets; user inputs relating to        his behaviors and schedules; user inputs relating to the        function and activities of the devices; other user information        available through a networked device such as contacts,        demographics, etc; GPS and other location signals such as WiFi        network IDs, names and signal strengths macrogrid outputs from        within a population in which user belongs; television and radio        signals; memory based historical consumption data    -   said data processor including means to create communications to        user based on information acquired from any of the sources; and    -   c) a user interface.        Preferably real time is within a five minute interval or less.        A non-transitory processor readable medium storing code        representing instructions to cause a processor to acquire,        catalogue and store power consumption data in respect to a first        energy consumption device (with an energy draw) within a        premises comprising a plurality of energy consumption devices        which comprises:    -   a) providing a sensor configured to measure at least one desired        energy consumption variable associated with the plurality of        energy consumption devices (including the first device) within        the premises and to generate at least one aggregated output        signal therefrom;    -   b) configuring a data processor to receive said aggregated        signal from the sensor;    -   c) creating a power profile for the first device by instructing        a user, via a user interface, to independently switch said        device between on-off positions (“switching set up”), at least        one time, to isolate a power draw for said device from the        aggregated signal, wherein data processor recognizes that the        first device was selected and isolates a differential in the        aggregate signal based on differing switch positions during the        switching set up, said differential being the energy draw of the        first device; and    -   d) providing a memory which recallably stores the energy draw of        the first device in a catalogue. Preferably, the code comprises        instructions to create a power profile for a second device by        instructing a user, via a user interface, to independently        switch said second device between on-off positions (“switching        set up”), at least one time, to isolate a power draw for said        second device from the aggregated signal, wherein data processor        recognizes that the second device was selected and isolates a        differential in the aggregate signal based on differing switch        positions during the switching set up, said differential being        the energy draw of the second device; and to provide a memory        which recallably stores the energy draw of the second device in        a catalogue.

An unsupervised system for use in creating a profile of, managing andunderstanding power consumption in a home of a user, wherein said homecomprises two or more power consuming devices which system comprises:

-   -   at least one sensor configured to measure aggregate energy        consumption at the home;    -   a mobile computing device comprising a data processor;    -   computer readable memory including computer readable        instructions which, when executed by the processor, cause the        processor to perform the following steps: i) receive said        aggregated signal from the sensor; ii) collect and record the        aggregate signal over a plurality of time resolutions and        frequencies, iii) create a predicted aggregate signal pattern        for each time x and frequency y; vi) to detect changes in the        predicted aggregate signal pattern at time x an frequency y        (detected consumption pattern changes); and    -   a communication interface operably connected to the mobile        computing device and configured for conveying to a user        notification of detected consumption pattern changes.

(A)

A system for use in creating a profile of, managing and understandingpower consumption in a home, wherein said home comprises two or morepower consuming devices which system comprises:

-   -   a) at least one sensor configured to measure at least one energy        consumption variable associated with at least one energy        consumption device within the home (“the selected device”) and        to generate at least one aggregated output signal therefrom;    -   b) a mobile computing device comprising a data processor;    -   c) computer readable memory comprising memory comprising a        catalogue of a plurality of devices and one of a respective or        estimated power draw of each such device, said memory including        computer readable instructions which, when executed by the        processor, cause the processor to perform the following        steps: i) receive said aggregated signal from the sensor; ii)        create and update a power profile for the selected device, iii)        collect and analyze raw data in real time, iv) calculate a delta        for each selected device (difference between an on state and an        off state); v) calculate an estimated delta for the selected        device, using ON-OFF-ON sequences (or OFF-ON-OFF) thereby        acquiring a start value and end value, and vi) comparing the        start value and end value to assess reliability of the estimated        delta for the selected device; and    -   d) a communication interface operably connected to the mobile        computing device and configured for receiving user commands and        queries, for requesting user input in respect to said devices        and for transmitting information relating to the devices to the        user.

(B)

Preferably, the systems at A above additionally comprise the features ofB. Preferably, the communication interface is selected from wired andwireless communication technologies. Preferably, the communicationinterface is selected from the group consisting of RS232, USB,Firewire™, Ethernet, Zigbee™, Wifi, Bluetooth™ RFJID, wireless USB,cellular, and WMAN communication technologies. Preferably, the dataprocessor creates a power profile for a first selected device byinstructing a user, via the interface, to independently switch saiddevice between on-off positions (“switching set up protocol”), at leastone time, to isolate a power draw for said device from the aggregatedsignal, and wherein data processor recognizes that the first device wasselected and to isolate a differential in the aggregate signal based ondiffering switch positions during the switching set up protocol, saiddifferential being the energy draw of the first device.

Preferably, the data processor repeats the switching set up protocol fora plurality of energy consumption devices in the home to create acatalogue of energy draws for such devices but wherein power consumptionof all devices in the home are not catalogued via the switching set upprotocol. Preferably, the communications interface provides a graphicrepresentation to the user of differentials in power output betweentoggled switch positions in switching set up protocol. Preferably, thecommunications interface provides a prompt to the user to toggle thedevice between on-off positions up more than once in switching set upprotocol in response to noise in graphic representation. Preferably,data processor removes noise by way of an averaging-median calculationof multiple differential measurements for the device or by directingtoggling of the device between on and off positions. Preferably, dataprocessor estimates reliability of a) information regarding energy drawof a selected device and b) device cost estimation, by directing a user,via the interface, to turn device on and off more than once and usingaveraging to remove noise. Preferably, data processor reports to theuser, via the interface at least one of the following: noise andpotential accuracy in device power consumption cost.

Preferably data processor disables “new” device profiling in presence ofexcessive noise. Preferably, data processor gathers additional data andremoves noise using noise removal techniques such as averaging or medianin order to compensate for noise. Preferably, processor reportsreliability of the estimated delta to a user, via the interface.Preferably, the memory recallably stores energy draws of the devices ina catalogue. Preferably, the processor receives feedback as to state(on-off) of a device. Preferably, the sensor is selected from a currentsensor, a voltage sensor, a temperature sensor, an activity sensor, andan acoustic sensor. Preferably, the interface is configured toproactively convey notifications to a user, such notifications beinggenerated by the processor in response to data analysis. Preferably, theprocessor monitors and analyzes user behaviors, and directs to the user,proactively and via interface, actionable information relating to one ormore of: savings potential, home safety recommendations and homesecurity recommendations.

Preferably notifications are generated based on at least one of: i)external events; ii) user-configured internal schedules; iii) feeds fromexternal processors-pushed to the processor; and iv) evaluationsperformed by the processor based on raw data inputs from at least oneof: smart grid networks; current sensors; user inputs relating touser-defined budgets; user inputs relating to his behaviors andschedules; user inputs relating to the function and activities of thedevices; other user information available through a networked devicesuch as contacts, demographics, etc; GPS and other location signals suchas WiFi network IDs, names and signal strengths; macrogrid outputs fromwithin a population in which user belongs; television and radio signalsand memory based historical consumption data.

Preferably, the interface is configured proactively to convey anotification to a user to turn off a device. Preferably, the interfaceis configured to proactively convey information to a user in regards toat least one of: predicted aggregate signal patterns, power consumptionbudgeting feedback; evaluated real-time consumption patterns; auser-defined budget, over-budget consumption warnings and under-budgetconsumption accolades. Preferably, the interface is configured toproactively convey notifications to a user, such notifications beinggenerated by the processor in response to data analysis suchnotifications: a) proactively reminding users of a “left-on” device; b)providing a breakdown of any devices left on by accident; c) relayingconsequences of “left-on” devices; and d) providing home securityfeedback to users. Preferably, the processor measures at least oneenergy consumption variable associated with at least one energyconsumption device within the home automatically and without a usertrigger/request. Preferably, the processor measures at least one energyconsumption variable associated with at least one energy consumptiondevice within the home automatically upon receipt of data indicating anoticeable consumption change is observed and b) to ask user to identifysource of such consumption change. Preferably, the processor asks a userfor additional information, including device classification, and timingand length of the periods of usage of device (e.g., minutes and hoursper day, days per months, etc.). Preferably, wherein a user creates apower profile for an energy consumption device by way of an applicationon a mobile processing device which application may be pre-installed onmobile devices during manufacture or can be downloaded byusers/customers from various mobile software distribution platforms, orweb applications delivered over, for example, HTTP which use server-sideor client-side processing (for example, JavaScript) to provide an“application-like” experience within a Web browser.

Preferably, the data processor monitors a user's ‘away from the home’hours based on usual power consumption patterns and stores data inmemory in this regard, such monitoring being based upon at least one ofthe following: specific triggers in real-time power consumptionindicative of whether a user is about to leave home; specific triggersin real-time power consumption indicative of whether a user has justleft home; user input via interface; cues from a user's computingplatform (including GPS signals); and external power signals (includingWi-Fi range and availability) and other metrics usable to gauge a user'sproximity to the home.

Preferably, the data processor gathers data incrementally by time (bytime of day, weekday vs. weekend, holiday vs. workday) and to identify auser's behaviors based on a) aggregated signal from the sensor; b) powerprofile for a selected device; c) data acquired directly/indirectlythrough a application on the mobile computing device platform; d) timeof day; e) day of the week and f) time of the year. Preferably,computing device has access to a memory, and the memory stores a recordof predicted aggregate signals and detected consumption pattern changes.Preferably, the catalogue so created can be used for consumer analytics:a) defining and classifying user demographics; b) modeling userconsumption behavior; c) forecasting utility bills; and d) forecastingconsumption (collectively “user classification”). Preferably, the systemand method of the invention are used to create targeted advertisements,targeted customer initiatives based on the customer classifications, andto design electric utility programs such as Demand Response based oncustomer classifications. Preferably, the method and system are deployedvia a mobile device application, and by which the user is connectable toother users of the application by a website or remote server and wherebythe user and other users exchange data and information.

Preferably, user and other users share user generated content includingintelligent conservation targeting strategies (based on user profile,demographic, consumption, and home catalogue information). Preferably,user and other users share comparative data based on a user's community,city, demographics, social circles and social media presence.Preferably, user and other users are connected to exchange informationbased upon at least one of common or similar community, city,demographics, social circles and social media presence to exchangeinformation on consumption and saving. Preferably, the data processor a)considers input signals and identifies actions of a user in switching onand off more than one device for the purpose of switching set up andcataloguing of all such devices; b) triggers a manual loaddisaggregation protocol; and c) identifies for the user at least oneselected appliance to expedite load disaggregation thereon. Preferably,the system comprises a proactive interface for display of at least onepiece of information on a home screen widget, a lock screen, and astatus bar. Preferably, the system comprises a processor which enablesenergy consumption device to mobile computing device communicationsincluding a familiarity detector which identifies “habit” information ofthe user, said habit information being usable to perform device relatedtasks in the home without user input.

Preferably, the system is enabled for energy consumption device tomobile computing device communications, wherein sensor data relating todocking or undocking of an energy consumption device to a power sourceis relayed to the processor to create at least one of a docking andundocking profile. Preferably, a processor which collects other userdata to aggregate with habit data and docking/undocking profile.Preferably, processor enables energy consumption device to mobilecomputing device communications including a familiarity detector whichcollects “habit” data in regards to the user, said processor performingtask without user input. Preferably, processor conveys userclassification data to a power utility company to identify a subset ofusers having a selected modeling user consumption behavior. Preferably,classification data provided to utility company enables utility companyto create targeted advertisements, targeted customer initiatives basedon the customer classifications, and to design electric utility programssuch as Demand Response based on customer classifications.

Preferably, the system is used to assist users in shared livingarrangements in order to apportion power usage for share billing.Preferably, the system additionally comprises user proffered personalinformation, entered through the interface of the mobile device.Preferably, the processor is configured to, with at least one of userdemographic data, user consumption behavior and forecasts; utility billforecasts, user historic patterns of power consumption, devise a budget.Preferably, processor is configured, based on at least one of userdemographic data, user consumption behavior and forecasts; utility billforecasts, user historic patterns of power consumption a) to calculatebudget balances at any time for a user; b) to calculate forecastedconsumption in a selected time; c) to calculate any deviation inforecast vs actual in regards to power consumption. Preferably,processor is configured to detect deviations in habit behavior of user,such deviations indicating an absence from the home. Preferably,processor is configured to engage a user in live, real-time socialevents relating to power consumption and power conservation. Preferably

A method for use in creating a profile of, managing and understandingpower consumption in a home of a user, wherein said home comprises twoor more power consuming devices which comprises:

-   -   measuring, via at least one sensor, aggregate energy consumption        at the home;    -   receiving at a mobile computing device comprising a data        processor, said aggregated signal from the sensor;    -   collecting and recording the aggregate signal over a plurality        of time resolutions and frequencies, therein to create a        predicted aggregate signal for each time x and frequency;    -   detecting changes in the predicted aggregate signal at time x an        frequency y (detected consumption pattern changes); and    -   conveying to at least one of the user, a utility company, and        other third party a notification of detected consumption pattern        changes.

Preferably, wherein predicted aggregate signal is a power consumptionforecast within the house for time x and frequency y and indicatesbehavioral patterns of the user (pattern of interest). Preferably, timeis measured in an increment selected from the group consisting ofsecond, minutes, hours, days, weeks, months, and years.

Preferably, predicted aggregate signal is a forecast of aggregate powerusage over a billing period (forecast bill) and wherein method comprisescalculating a forecast bill based on said predicted aggregate signal;comparing an actual bill over the billing period, assessing performanceby comparing forecast bill to actual bill as follows:

$B_{R} = {{\sum\limits_{t \in P}\; {C_{t^{\prime}}\mspace{45mu} B_{F}}} = {\sum\limits_{t \in P}\; F_{i}}}$$e_{P} = {{{B_{F} - B_{R}}} = {{\sum\limits_{i \in P}\; \left( {F_{i} - C_{i}} \right)}}}$

where C is hourly consumption, F is hourly forecast, B_(R) is realbilling cost, B_(F) is the forecast bill, P is billing period, and e_(p)is forecast error of the billing period period.

Preferably, wherein patterns exist at different time intervals andfrequencies and wherein consumption data provided in a resolution, ispresented by C^(α):

C ^(α) ={C ₁ ^(α) ,C ₂ ^(α) ,C ₃ ^(α) , . . . ,C _(N) ^(α)}

Preferably to resolve a correct time for a pattern of interest, β:

${{new}\mspace{14mu} {size}\mspace{14mu} N} = \frac{N}{\beta}$C^(β) = {C₁^(β), C₁^(β), …  , C_(N)^(β)}$C^{\beta} = {\left. \left\{ {{\sum\limits_{i = 1}^{\frac{\beta}{\alpha}}\; C_{i}^{\alpha}},{\sum\limits_{i = {\frac{\beta}{\alpha} + 1}}^{2 \cdot \frac{\beta}{\alpha}}\; C_{i}^{\alpha}},\ldots \mspace{14mu},{\sum\limits_{i = {{{({N - 1})} \cdot \frac{\beta}{\alpha}} + 1}}^{N \cdot \frac{\beta}{\alpha}}\; C_{i}^{\alpha}}} \right\}\rightarrow k \right. = {\left\lbrack {1,\overset{.}{N}} \right\rbrack \text{:}}}$$C_{k}^{\beta} = {\sum\limits_{i = {{{({k - 1})} \cdot \frac{\beta}{\alpha}} + 1}}^{k \cdot \frac{\beta}{\alpha}}\; C_{i}^{\alpha}}$

new size N′=N/β,

Ĉβ={C_1̂β,C_2̂β, . . . ,C_N′̂β}

calculating mean (μ) and deviation (s) of each β-sized time interval(t), within the period length P;

a)  P;${{{for}\mspace{14mu} t} = {{\left\lbrack {1,\frac{p \cdot \alpha}{\beta}} \right\rbrack \mspace{14mu} {and}\mspace{14mu} d} = {\left\lfloor \frac{\overset{\prime}{N}}{\frac{p \cdot \alpha}{\beta}} \right\rfloor = \left\lfloor \frac{N}{P \cdot \alpha} \right\rfloor}}},{\mu_{t} = {\frac{1}{d}{\sum\limits_{i = 0}^{d - 1}\; C_{({{i \cdot d} + t})}^{\beta}}}},\mspace{31mu} {s_{t} = \sqrt{\frac{1}{d - 1}{\sum\limits_{i = 0}^{d - 1}\; \left( {C_{({{i \cdot d} + t})}^{\beta} - \mu_{t}} \right)^{2}}}}$

wherein β≧α since a desired pattern resolution is never smaller than anoriginal data's resolution.

Preferably, forecasting consumption is based on mean and standarddeviation and wherein a low standard deviation (s_(t)) indicates ahighly repetitive behavior in the given time resolution and offset, ahigh deviation indicates no significance pattern.

${B_{R} = {\sum\limits_{i \in P}\; C_{i}}},\mspace{31mu} {B_{F} = {\sum\limits_{i \in P}\; F_{i}}}$$e_{P} = {{{B_{F} - B_{R}}} = {{\sum\limits_{i \in P}\; \left( {F_{i} - C_{i}} \right)}}}$

where C is the hourly consumption, F is the hourly forecast, B_(R) isthe real billing cost, B_(F) is the forecasted bill, P is the billingperiod, and e_(p) is the forecast error of the given period.Preferably, the method additionally comprises an analysis of consumptiontrends (predicted rate of change in consumption patterns) in the housewhich comprises:

-   -   wherein trends can be examined at different time-resolutions and        polynomial orders and wherein a lower time-resolution (large 13        values) make the trend analysis less sensitive to noise (highly        deviated data with insignificant forecasting value) and wherein        higher polynomial orders are more responsive to change, but also        more sensitive to noise, adjusting the consumption data's        resolution;    -   using linear regression is used to detect the trend:        n: polynomial order,

c=α ₀ ·x ^(n)+α₁ ·x ^(n-1)+ . . . +α_(n-1) ·x+α _(n)

wherein x is the time and c is the consumption and wherein theleast-squared solution to the above polynomial is:m: data points,

$\begin{bmatrix}C_{1} \\C_{2} \\\vdots \\C_{m}\end{bmatrix} = {\left. {\begin{bmatrix}1 & x_{1} & x_{1}^{2} & \ldots & x_{1}^{n} \\1 & x_{2} & x_{2}^{2} & \ldots & x_{2}^{n} \\\vdots & \vdots & \vdots & \ddots & \vdots \\1 & x_{m} & x_{m}^{2} & \ldots & x_{m}^{n}\end{bmatrix}\begin{bmatrix}a_{0} \\a_{1} \\\ldots \\a_{n}\end{bmatrix}}\rightarrow Y \right. = {XA}}$X^(T)C = X^(T)XA ⇒ A = (X^(T)X)⁻¹X^(T)C

-   -   a) determining consumption at a given time (x)

first order: tr(x)=α₀ ·x+α ₁

n-th order; tr(x)=α₀ ·x ^(n)+α₁ ·x ^(n-1)+ . . . +α_(n-1) ·x+α _(n)

-   -   b) measuring accuracy of an estimated trend line

${ESS} = {{\sum\limits_{i = 1}^{m}\; \left( {{{tr}\left( x_{i} \right)} - c_{i}} \right)^{2}} = {{C^{T}C} - {\left( {X^{T}X} \right)^{- 1}X^{T}C\mspace{11mu} X^{T}C}}}$

A method of integrating the patterns before applying a trends analysiscomprises:

-   -   a) for k patterns and trends,

^(u)μ_(x),^(u) s _(x): mean and standard deviation at time x for patternu

^(v)tr(x),^(v)ESS: trend estimate and error at time x for trend v

$\left\{ {{{\begin{matrix}{{u\text{:}\mspace{14mu} {pattern}},} & {{{}_{}^{}{}_{}^{}} = {{}_{}^{}{}_{}^{}}} \\{{u\text{:}\mspace{14mu} {trend}},} & {{{}_{}^{}{}_{}^{}} = {{\,^{u}{tr}}(x)}}\end{matrix}{w(x)}} = {\sum\limits_{v = 1}^{k}\; \frac{1}{{}_{}^{}{}_{}^{}}}},\mspace{31mu} {{P(x)} = {\sum\limits_{u = 1}^{k}\; \frac{{{}_{}^{}{}_{}^{}} \cdot \frac{1}{{}_{}^{}{}_{}^{}}}{w(x)}}}} \right.$

wherein w(x) represents the total weight of all pattern forecasts attime x, and f(x) represents the final forecast value for allpatterns/trends of the same time-resolution and wherein patterns/trendsof varying resolution are converted to the lowest time-resolution:

p^(α) = {p₁^(α), p₂^(α), …  , p_(N)^(α)},  α:  resolution, β:  new  resolution, β > α$\left\{ {\left. {\forall{{i\text{:}\mspace{14mu} {\left( {t - 1} \right) \cdot \frac{\beta}{\alpha}}} < x_{i} \leq {t \cdot \frac{\beta}{\alpha}}}} \middle| {{}_{}^{}{}_{}^{}} \right. = {\sum\limits_{i}\; {{}_{}^{}{}_{}^{}}}} \right\}$${P(x)} = {{{}_{}^{}{}_{}^{}} \cdot \frac{{}_{}^{}{}_{}^{}}{{}_{}^{}{}_{t\text{:}\mspace{14mu} \left( {x \in t} \right)}^{}}}$${\overset{\prime}{S}}_{x}^{\alpha} = {{{}_{}^{}{}_{}^{}} \cdot \frac{{}_{}^{}{}_{}^{}}{{}_{}^{}{}_{t\text{:}\mspace{14mu} \left( {x \in t} \right)}^{}}}$

A method comprises the following steps:

-   -   a) integrate all patterns of the highest resolution;    -   b) integrate no patterns/trends at lower resolution;    -   c) integrate all patterns at next highest resolution;    -   d) convert patterns/trends of varying resolution to the lowest        time-resolution; and    -   e) repeat steps a) to d) until no low resolution patterns        exists.

Preferably, the utility receives information relating to detectedconsumption pattern changes and then directs notification to the user ofat least one of messages selected from the group consisting in whole orpart of:

-   -   a grid within which home is located is experiencing an unusual        over-consumption    -   a request to user to turn off at least one power consuming        device.

Preferably, the utility company offers an incentive to user to turn offat least one power consuming device. Preferably, said incentive isselected from the group consisting of cash and prizes. Preferably, theprocessor is configured within a mobile computing device selected fromthe group consisting of a Smartphone, tablet, netbook and laptop, anIn-Home Display (IHD) platform and a home-energy management device.

Preferably, a mobile application runs on mobile computing device andenables operation of the method and wherein utility company and userinteract via mobile application. Preferably, a mobile application runson mobile computing device and enables operation of the method.Preferably, the notification of detected consumption pattern changes isconveyed via a communication interface selected from the groupconsisting of RS232, USB, Firewire™, Ethernet, Zigbee™, Wifi,Bluetooth™, RFJID, wireless USB, cellular, and WMAN communicationtechnologies.

1. A system for monitoring and analyzing electricity consumption in ahome of a user, the home comprising multiple electricity consumingdevices, the system comprising: one or more electricity sensorsconfigured to measure electricity consumption at the home; a mobilecomputing device; a processor in the mobile computing device, configuredto receive signals from the sensors; and a user interface on the mobilecomputing device; wherein the processor is configured to: determineindividual electricity consumptions of the devices without there being asensor on each device; identify a consumption change of one of thedevices; and send a notification of the consumption change to the userinterface.
 2. The system of claim 1, wherein the consumption change is achange from a usual consumption pattern.
 3. The system of claim 1,wherein the processor is further configured to: identify a consumptionpattern change of the home; determine that the consumption patternchange represents an intrusion; and send a notification of saidconsumption pattern change to a security system.
 4. The system of claim1, wherein the processor is further configured to: identify aconsumption pattern change of the home; and send a notification of theconsumption pattern change to the user interface.
 5. The system of claim4, wherein the consumption pattern has a duration of a day or a week. 6.The system of claim 5, wherein: when the consumption pattern has aduration of a day, the consumption pattern has a resolution of an hour;and when the consumption pattern has a duration of a week, theconsumption pattern has a resolution of a day.
 7. The system of claim 1,wherein: the processor is further configured to determine that theconsumption change represents a hazard; and the notification isinformative of the hazard.
 8. The system of claim 7, wherein theprocessor determines that the consumption change corresponds to one ofthe devices being left switched on.
 9. The system of claim 1, whereinthe processor is further configured to present, on the user interface:devices that are offered by third party retailers and are alternate tosaid one device; and a savings value of each device offered.
 10. Thesystem of claim 9, wherein the notification indicates either that saidone device may be broken or that said one device may be old.
 11. Thesystem of claim 1, wherein the processor is further configured to:determine a behavioral pattern of the user based on the electricityconsumption of the devices; and trigger one of the devices based on thebehavioral pattern.
 12. The system of claim 2, wherein the processor isconfigured to: identify an overconsumption of electricity by the homeover a first time period; calculate whether an overconsumption ofelectricity by the home will occur over a second time period thatincludes the first time period; and identify said consumption patternchange by determining that overconsumption of electricity by the homewill occur over the second time period.
 13. The system of claim 2,wherein said consumption pattern change is an overconsumption ofelectricity by the home that is greater than a budgeted tolerance inoverconsumption of electricity by the home.
 14. The system of claim 2,wherein the usual consumption pattern is trending.
 15. The system ofclaim 14, wherein the processor calculates the usual consumption patternusing signals from the sensors that are not older than a predeterminedamount of time.
 16. A method for monitoring and analyzing electricityconsumption in a home of a user, the home comprising multipleelectricity consuming devices, the method comprising: configuring one ormore electricity sensors to measure electricity consumption at the home,without there being a sensor on each device; receiving, by a processorin a mobile computing device, signals from the one or more sensors;determining, by the processor, individual electricity consumptions ofthe devices; identifying a consumption change of one of the devices; andsending a notification of the consumption change to a user interface onthe mobile computing device.
 17. The method of claim 16, furthercomprising the processor: identifying a consumption pattern change ofthe home; determining that the consumption pattern change represents anintrusion; and sending a notification of said consumption pattern changeto a security system.
 18. The method of claim 16, further comprising theprocessor determining that the consumption change represents a hazard,wherein the notification is informative of the hazard.
 19. The method ofclaim 16, further comprising the processor: presenting, on the userinterface, devices that are offered by third party retailers and arealternate to said one device; and presenting, on the user interface, asavings value of each device offered.
 20. The method of claim 16,further comprising the processor: determining a behavioral pattern ofthe user based on the electricity consumption of the devices; andtriggering one of the devices based on the behavioral pattern.